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Instead, individuals sit at the intersection of different social dimensions, and this needs to be considered when assessing who is at higher risk of not attending BCS. Methodologically and conceptually, the way the risk for not attending BCS of a person with a migration background and low educational attainment can be seen differently: either as the sum of (presumably) independent discrimination dimensions or as accounting for the discrimination of being a migrant from a lower social class simultaneously . It is, therefore, essential to employ a framework that allows to detect the inherent complexity of inequalities when attempting to understand the underlying factors influencing BCS attendance. The most appropriate approach is to adopt the framework of intersectionality . Intersectionality theory, as first proposed by law scholar Kimberlé Crenshaw in 1989, posits that the experiences of discrimination (e.g. classism, racism) based on disadvantaged social positions (e.g. low social class, migration background) overlap and derive into unique experiences of discrimination .
|
PMC11699213_p5
|
PMC11699213
|
Introduction
| 2.886972 |
other
|
Other
|
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Over the past two decades, in the field of population health, quantitative intersectionality has given rise to new methodological approaches. The most commonly used methods for describing intersectional inequalities within a population range from simple cross-classification descriptions or regressions to methods that account for discriminatory accuracy (e.g. analysis of individual heterogeneity and discriminatory accuracy (AIHDA) and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)) or are data-driven (e.g. decision trees) .
|
PMC11699213_p6
|
PMC11699213
|
Introduction
| 3.664 |
biomedical
|
Review
|
[
0.9571534991264343,
0.0009019523859024048,
0.04194458946585655
] |
[
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en
| 0.999998 |
To build cross-classification regression and AIHDA or MAIHDA, the (potentially) relevant social dimensions are usually selected on the basis of the available evidence, and these dimensions are combined to identify intersectional subgroups. This is a deductive approach. In contrast, decision trees and analogous heuristic procedures employ an inductive methodology to identify which variables are most predictive of an outcome assuming non-linear relationships between the variables . This enables a data-driven determination of the social dimensions that will constitute intersectional subgroups, often previously unnoticed . Decision trees have been applied as statistical exploratory tools for classification in population health .
|
PMC11699213_p7
|
PMC11699213
|
Introduction
| 3.655246 |
biomedical
|
Study
|
[
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0.0005193323013372719,
0.06900910288095474
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[
0.6514842510223389,
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To the author's knowledge, no explicit comparisons between these approaches to identify intersectional inequalities in breast cancer screening have been conducted; besides, no study has employed an intersectional approach for reporting inequalities in breast cancer screening in Germany. Consequently, the present study aims to identify intersectional groups of women aged 50–69 who are at higher risk of never attending BCS in Germany comparing two analytical strategies: a) evidence-informed regression and b) decision tree-based regression.
|
PMC11699213_p8
|
PMC11699213
|
Introduction
| 3.846583 |
biomedical
|
Study
|
[
0.9965218305587769,
0.0004181053664069623,
0.0030600696336477995
] |
[
0.9994733929634094,
0.0002525287272874266,
0.0002331001014681533,
0.00004107772474526428
] |
en
| 0.999998 |
For this analysis, we employed cross-sectional data from the European Health Interview Survey (EHIS) third wave conducted in Germany in 2019. The survey sample size was 23,001 respondents, corresponding to 21.6% of the invited participants . EHIS is conducted every 5 years and focuses on individuals aged 15 and above residing in private households .
|
PMC11699213_p9
|
PMC11699213
|
European Health Interview Survey
| 2.12578 |
biomedical
|
Study
|
[
0.9640954732894897,
0.0012380687985569239,
0.03466654196381569
] |
[
0.9927207231521606,
0.00674560246989131,
0.000337662931997329,
0.00019613005861174315
] |
en
| 0.999996 |
The primary outcome of this study was self-reported breast cancer screening attendance via mammography at least once in a lifetime for women aged 50–69 in Germany. Responses were dichotomised, excluding those who indicated “unknown” or left the question unanswered to prevent uncertainty about whether the respondent reported never attending BCS (no = 0, yes = 1).
|
PMC11699213_p10
|
PMC11699213
|
Primary outcome
| 3.365308 |
biomedical
|
Study
|
[
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0.001588690560311079,
0.0010988481808453798
] |
[
0.9991344809532166,
0.000600750558078289,
0.00016425331705249846,
0.00010053873847937211
] |
en
| 0.999996 |
The explanatory variables to predict BCS derive from the PROGRESS-Plus characteristics: place of residence, race, ethnicity, culture and language, occupation, sex, education, socioeconomic status, social capital and plus (i.e. other potentially discriminatory factors) . These variables have been widely used to disentangle social inequities in health .
|
PMC11699213_p11
|
PMC11699213
|
Explanatory variables
| 2.162663 |
biomedical
|
Study
|
[
0.978552520275116,
0.0007704416057094932,
0.020676983520388603
] |
[
0.5896098017692566,
0.3990786075592041,
0.010526750236749649,
0.0007848451496101916
] |
en
| 0.999997 |
Place of Residence was determined through the degree of urbanisation of the municipality and the specific region ( Bundesland ). The first variable was composed of three categories: cities (densely populated areas), towns and suburbs (intermediate-density areas), and villages (thinly populated areas). The second variable indicated the federal states ( Bundesländer ) in Germany.
|
PMC11699213_p12
|
PMC11699213
|
Explanatory variables
| 1.416028 |
other
|
Other
|
[
0.17245592176914215,
0.0011443640105426311,
0.8263997435569763
] |
[
0.3177298307418823,
0.6801803112030029,
0.0009546726942062378,
0.0011351787252351642
] |
en
| 0.999995 |
Race, ethnicity, culture, and language were indicated by proxy variables: since the EHIS did not assess either of these explicitly. We selected the respondent's country of origin and nationality and then classified them as either born in Germany, in Europe or outside Europe. Although short at measuring complexities of identity, these variables have shown utility as ethnicity proxies in European countries where no information on race or ethnicity is gathered .
|
PMC11699213_p13
|
PMC11699213
|
Explanatory variables
| 1.616578 |
other
|
Study
|
[
0.3094514310359955,
0.001284637488424778,
0.6892638802528381
] |
[
0.803642749786377,
0.19449862837791443,
0.0010495439637452364,
0.000809083750937134
] |
en
| 0.999995 |
Occupation was operationalised based on the respondents’ current working situation: in paid employment, unemployed, retired, unable to work, (unpaid) household work and others.
|
PMC11699213_p14
|
PMC11699213
|
Explanatory variables
| 1.209596 |
other
|
Other
|
[
0.07796751707792282,
0.001427055336534977,
0.920605480670929
] |
[
0.2584998905658722,
0.7377960681915283,
0.0019136543851345778,
0.0017903796397149563
] |
en
| 0.999998 |
Sex (to identify as a female) was a prerequisite for participant inclusion in the analysis. Gender and religion were not captured by the EHIS.
|
PMC11699213_p15
|
PMC11699213
|
Explanatory variables
| 1.713121 |
biomedical
|
Study
|
[
0.9354532957077026,
0.002613047370687127,
0.061933714896440506
] |
[
0.8470478653907776,
0.150987446308136,
0.0009748990414664149,
0.0009897294221445918
] |
en
| 0.999995 |
Education was measured following the ISCED-2011 classification . Since only 6 participants had primary education or less, the first three categories were combined into “less than upper secondary education”.
|
PMC11699213_p16
|
PMC11699213
|
Explanatory variables
| 1.659035 |
biomedical
|
Study
|
[
0.6866068840026855,
0.0017223514150828123,
0.3116707503795624
] |
[
0.8764363527297974,
0.1218227967619896,
0.0010707674082368612,
0.0006700129015371203
] |
en
| 0.999997 |
Socioeconomic status was operationalised through household income and was divided into five quintile groups: the 20% with the lowest income were coded 1, and the 20% with the highest income were coded 5 .
|
PMC11699213_p17
|
PMC11699213
|
Explanatory variables
| 1.746747 |
biomedical
|
Study
|
[
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0.0020873500034213066,
0.13109982013702393
] |
[
0.8807166814804077,
0.11758095026016235,
0.0009646161342971027,
0.0007377054425887764
] |
en
| 0.999998 |
Social capital was considered through six variables: social network dimensions (none, 1–2, 3–5, 6 or more), perceived social support (a lot, some, uncertain, little, or no concern) and ease in available help (very easy, easy, possible, difficult, or very difficult). Further, three proxy variables were also included: marital status (single, married, legally separated/divorced or widowed), type of household (alone, with a partner, with a partner and children, with children, or other) indicating the availability of family support, and partner cohabitation (yes or no).
|
PMC11699213_p18
|
PMC11699213
|
Explanatory variables
| 1.794843 |
other
|
Study
|
[
0.3610469698905945,
0.0015686115948483348,
0.6373844742774963
] |
[
0.9790216684341431,
0.01997048780322075,
0.0006877469713799655,
0.00032010237919166684
] |
en
| 0.999996 |
For the Plus dimension, the Global Activity Limitations Indicator (GALI), a self-report of the extent of limitation experienced in the last six months was considered, with possible answers: severely limited, mildly limited, or not limited . Age (50–69 years old) was required to be included in the analysis and was treated as a confounder in the regression analyses.
|
PMC11699213_p19
|
PMC11699213
|
Explanatory variables
| 2.631799 |
biomedical
|
Study
|
[
0.9937084913253784,
0.0013324974570423365,
0.004958925303071737
] |
[
0.9971612691879272,
0.002479543210938573,
0.00022690618061460555,
0.0001323452452197671
] |
en
| 0.999997 |
Descriptive analytics, including frequencies and percentages, were calculated for all variables. A complete case analysis was conducted, i.e. cases with missing data were excluded listwise. The total sample was restricted to women aged 50–69 . Among these women, those who did not respond on whether they underwent mammography (n = 15), their place of residence (n = 384), the degree of urbanisation of their place of residence (n = 213), the household's income (n = 122), their level of education (n = 14), their social network dimensions (n = 11), their perceived social support (n = 46), the available help (n = 81), the type of household (n = 56), their marital status (n = 13), their partnership cohabitation status (n = 30), their working situation (n = 10), their country of origin (n = 11), their citizenship (n = 7), their GALI (n = 7) were excluded. Hence, the final total sample size of the study was 4761 participants.
|
PMC11699213_p20
|
PMC11699213
|
Analytic approach
| 3.675934 |
biomedical
|
Study
|
[
0.9979518055915833,
0.0006399523699656129,
0.0014082894194871187
] |
[
0.9995922446250916,
0.00027089272043667734,
0.00009054371912498027,
0.00004633212665794417
] |
en
| 0.999997 |
Sampling weights were not used in analyses, as the sampling weights provided in the German EHIS data were derived from variables included in the analyses (education, urbanisation and age), which could lead to multicollinearity and biased standard error estimation. We report sensitivity analyses applying the sampling weights in both analytical strategies in Appendix A and show the correlations between sampling weights and variables in the analyses in Appendix B . The central aim of this article was to compare the estimation of women at higher risk of never attending BCS using two different analytical strategies: (a) evidence-informed regression and (b) decision tree-based regression.
|
PMC11699213_p21
|
PMC11699213
|
Analytic approach
| 3.313433 |
biomedical
|
Study
|
[
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0.0006029200158081949,
0.002860784064978361
] |
[
0.9993801116943359,
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0.00015836914826650172,
0.00005591490844381042
] |
en
| 0.999995 |
The evidence-informed analytical strategy builds a full cross-classification matrix based on social dimensions identified as relevant in the literature. A recent scoping review pinpointed migration background, socioeconomic position (based on income), degree of urbanisation, and partner cohabitation as significant dimensions for BCS attendance prediction .
|
PMC11699213_p22
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 2.444877 |
biomedical
|
Review
|
[
0.8803834915161133,
0.0026834867894649506,
0.11693309247493744
] |
[
0.4176454544067383,
0.0329788513481617,
0.5478602647781372,
0.0015154173597693443
] |
en
| 0.999995 |
For this analysis, country of origin was dichotomised as born inside or outside Germany, income was dichotomised into low (categories 1 and 2) and high (categories 3, 4 and 5), degree of urbanisation was dichotomised in people living in cities (urban) and people living in towns, suburbs or rural areas (rural), and partner cohabitation was already a dichotomous variable (yes/no). The cross-classification of all social positions led to 16 intersectional groups: 2 (country of origin) ∗ 2 (income) ∗ 2 (degree of urbanisation) ∗ 2 (partner cohabitation) ( Table 1 ). Table 1 Evidence-informed intersectional groups on lifetime BCS attendance. Table 1 Country of origin Income Degree of urbanisation Partner cohabitation Intersectional group name Germany High Urban Yes HGUY No HGUN Rural Yes HGRY No HGRN Low Urban Yes LGUY No LGUN Rural Yes LGRY No LGRN Other than Germany High Urban Yes HOUY No HOUN Rural Yes HORY No HORN Low Urban Yes LOUY No LOUN Rural Yes LORY No LORN
|
PMC11699213_p23
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 2.376462 |
biomedical
|
Study
|
[
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0.0015646711690351367,
0.25016677379608154
] |
[
0.9963394403457642,
0.003302958095446229,
0.00024020169803407043,
0.00011750867997761816
] |
en
| 0.999998 |
For the purpose of comparison, univariate models were initially constructed for each of the four individual predictors and age. Next, a multivariable model that included all main effects was estimated.
|
PMC11699213_p24
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 3.017295 |
biomedical
|
Study
|
[
0.9958648681640625,
0.0005494357901625335,
0.0035857628099620342
] |
[
0.9982624650001526,
0.0011984329903498292,
0.00044992376933805645,
0.0000892204261617735
] |
en
| 0.999996 |
Following this, a multivariate logistic regression with the full cross-classification matrix as the main predictor was performed to estimate the odds ratio (OR) of never attending BCS adjusted by age. Discriminatory accuracy (DA) was estimated through the area under the receiver operating characteristics curve (AUC) with a 95% confidence interval (CI), indicating how well each model discriminates between women attending and women never attending BCS. DA is considered absent or very small when 0.5 ≤AUC≤ 0.6, moderate when 0.6< AUC ≤0.7, large when 0.7< AUC ≤0.8 and very large AUC>0.8 . These statistical procedures were carried out using Stata version 17.0.
|
PMC11699213_p25
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 4.059789 |
biomedical
|
Study
|
[
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0.0003967055235989392,
0.0003450067015364766
] |
[
0.9993851184844971,
0.00026927769067697227,
0.000291690812446177,
0.000053960666264174506
] |
en
| 0.999994 |
The second analytical strategy consisted of two steps. First, building an explorative decision tree with the total sample size to identify homogeneous subgroups of women at higher risk of never attending BCS in Germany. Second, performing a multivariate logistic regression using the outcome of the decision tree adjusted by age to estimate the OR of never attending BCS.
|
PMC11699213_p26
|
PMC11699213
|
Analytical strategy b: decision tree-based regressions
| 3.894325 |
biomedical
|
Study
|
[
0.9990373849868774,
0.0004279125714674592,
0.0005347570986486971
] |
[
0.9992870688438416,
0.0004384583153296262,
0.00022233287745621055,
0.00005221804167376831
] |
en
| 0.999996 |
There is no consensus on which decision tree better operates on binary outcomes. In this study, we trained three different algorithms: Classification and Regression Tree (CART), Conditional Inference Tree (CIT) and C5.0. The CART algorithm makes splitting decisions based on the lowest gini impurity (or entropy) coefficient among all potential splits (i.e. every category or step of every variable) . CART does not provide statistical significance measures and potentially overestimates the influence of variables with many categories. CIT addresses these limitations by utilising a formal statistical hypothesis in growing decision trees and mitigating variable selection bias by splitting the selection process into two steps . C5.0 uses the entropy coefficient of the imputed variables to generate splits plus adaptative boosting and winnowing .
|
PMC11699213_p27
|
PMC11699213
|
Analytical strategy b: decision tree-based regressions
| 4.020599 |
biomedical
|
Study
|
[
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0.00031062751077115536,
0.008687086403369904
] |
[
0.9993066787719727,
0.00039490373455919325,
0.0002672579721547663,
0.000031186515116132796
] |
en
| 0.999998 |
All three decision tree algorithms (CART, CIT, C5.0) were built using the entire dataset and the same subdivision of the data when performing cross-validation. Cost weights were applied to distribute the sums of weights equally for cases and non-cases, given the (relative) rareness of the outcome in the dataset (10.38% prevalence). Parameters were hypertuned and optimised by two performance measures: sensitivity (i.e. enhancing detection of positive cases) and the Area Under the Precision-Recall Curve (i.e. improving overall precision-recall performance for unbalanced datasets) . Decision trees were grown using the tune function from the “mlr3tuning” optimisation R packages in R version 4.4.0. This package integrates essential packages for building CART “rpart” , CIT “partykit” , and C5.0 “C50” .
|
PMC11699213_p28
|
PMC11699213
|
Analytical strategy b: decision tree-based regressions
| 4.057607 |
biomedical
|
Study
|
[
0.999180257320404,
0.00023783621145412326,
0.000581839238293469
] |
[
0.9991123080253601,
0.0005429346929304302,
0.000304856599541381,
0.00003985732109867968
] |
en
| 0.999997 |
After inductively identifying the best-performing decision tree, the final nodes were deductively used as predictors for a multivariate logistic regression adjusted by age, where the ORs and DA of the model were estimated. This statistical procedure was performed using the Stata version 17.0. Estimations, performance and interpretability of both analytical strategies were compared and discussed.
|
PMC11699213_p29
|
PMC11699213
|
Analytical strategy b: decision tree-based regressions
| 3.816376 |
biomedical
|
Study
|
[
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0.00022524093219544739,
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] |
[
0.9991944432258606,
0.0005584555910900235,
0.00019608999718911946,
0.00005106988101033494
] |
en
| 0.999997 |
Summary descriptive statistics of the sample can be found in Table 2 . The total sample size is 4761. Of those, 4267 attended BCS at least once in their lifetime, and 494 did not. Relative frequencies for never attending BCS among the different PROGRESS-Plus characteristics were assessed. As expected, women aged 65–69 had the lowest prevalence (6.55%), and women aged 50–54, had the highest prevalence (18.58%). For SES, women in the lowest quintile attended BCS the least (14.07%), and those in the highest quintile attended BCS the most (9.79%). Almost contradicting, women with the highest education attainment, doctoral or equivalent, attended BCS the least (14.75%) and women with bachelor or equivalent educational attainment the most (9.10%). Based on the country of origin, women born in Germany (10.56%) had the lowest attendance rates compared to women born elsewhere. However, women of another European nationality attended the least (12.05%) and women of German nationality the most (10.31%). Regarding the place of residence, women living in cities (11.16%), women living in Berlin (13.69%) and Saarland (13.39%) had the lowest BCS attendance rate. Table 2 Descriptive PROGRESS-Plus characteristics on BCS attendance among targeted women in Germany. Relative frequencies per column and variable are displayed. Table 2 Attended BCS Never attended BCS (N = 494) Total Age 50-54 916 (21.5%) 209 (42.3%) 1125 (23.6%) 55-59 1149 (26.9%) 116 (23.5%) 1265 (26.6%) 60-64 1104 (25.9%) 92 (18.6%) 1196 (25.1%) 65-69 1098 (25.7%) 77 (15.6%) 1175 (24.7%) Income 1Q 458 (10.7%) 75 (15.2%) 533 (11.2%) 2Q 687 (16.1%) 76 (15.4%) 763 (16.0%) 3Q 795 (18.6%) 89 (18.0%) 884 (18.6%) 4Q 1010 (23.7%) 111 (22.5%) 1121 (23.5%) 5Q 1317 (30.9%) 143 (28.9%) 1460 (30.7%) Educational group Lower secondary or lower 219 (5.1%) 28 (5.7%) 247 (5.2%) Upper secondary 1515 (35.5%) 170 (34.4%) 1685 (35.4%) Post-secondary 609 (14.3%) 78 (15.8%) 687 (14.4%) Bachelor or equivalent 1120 (26.2%) 112 (22.7%) 1232 (25.9%) Master or higher a 804 (18.8%) 106 (21.4%) 900 (19.2%) Country of origin Germany 3948 (92.5%) 466 (94.3%) 4414 (92.7%) Outside of Germany a 319 (7.5%) (28) b (5.6%) 347 (7.3%) Citizenship German or other a 4267 (100%) 494 (100%) 4761 (100%) Degree of urbanisation City 1712 (40.1%) 215 (43.5%) 1927 (40.5%) Town or suburb 1803 (42.3%) 210 (42.5%) 2013 (42.3%) Rural area 752 (17.6%) 69 (14.0%) 821 (17.2%) Region Baden-Württemberg 459 (10.8%) (43) b (8.7%) 502 (10.5%) Bavaria 522 (12.2%) 71 (14.4%) 593 (12.5%) Berlin/Brandenburg a 503 (11.8%) 83 (14.7%) 576 (12.1%) Hesse 260 (6.1%) (31) b (6.3%) 291 (6.1%) Lower Saxony/Bremen a 368 (8.8%) (35) b (7.1%) 403 (8.5%) North Rhine-Westphalia/Rhineland-Palatinate a 991 (23.2%) 102 (20.6%) 1093 (22.9%) Saarland 427 (10.0%) 66 (13.4%) 493 (10.4%) Saxony/Saxony-Anhalt/Thuringia a 377 (8.8%) (37) b (7.4%) 414 (8.7%) Schleswig-Holstein/Hamburg/Mecklenburg-Vorpommern a 360 (8.4%) (36) b (7.2%) 397 (8.3%) Quality of social network 1–2 or less a 557 (13.1%) 82 (16.6%) 639 (13.5%) 3-5 2086 (48.9%) 252 (51.0%) 2338 (49.1%) >6 1624 (38.1%) 160 (32.4%) 1784 (37.5%) Perceived social support A lot 930 (21.8%) 116 (23.5%) 1046 (22.0%) Some 2529 (59.3%) 260 (52.6%) 2789 (58.6%) Uncertain 523 (12.3%) 77 (15.6%) 600 (12.6%) Little or none a 275 (6.7%) (41) b (8.3%) 326 (6.8%) Available help Very easy 1414 (33.1%) 165 (33.4%) 1579 (33.2%) Easy 1702 (39.9%) 176 (35.6%) 1878 (39.4%) Possible 726 (17.0%) 93 (18.8%) 819 (17.2%) Difficult 291 (6.8%) (34) b (6.9%) 325 (6.8%) Very difficult 134 (3.1%) (26) b (5.3%) 160 (3.4%) Marital status Single 481 (11.3%) 95 (19.2%) 576 (12.1%) Married 2706 (63.4%) 267 (54.0%) 2973 (62.4%) Widowed 449 (10.5%) (37) b (7.5%) 486 (10.2%) Divorced 631 (14.8%) 95 (19.2%) 726 (15.2%) Type of household Alone 1219 (28.6%) 168 (34.0%) 1387 (29.1%) With children 145 (3.4%) (21) (4.3%) 166 (3.5%) With a partner 2100 (49.2%) 172 (34.8%) 2272 (47.7%) With a partner and children 435 (10.2%) 86 (17.4%) 521 (10.9%) Other 368 (8.6%) (47) b (9.5%) 415 (8.7%) Working situation In paid employment 2531 (59.3%) 332 (67.2%) 2863 (60.1%) Unemployed/Others a 135 (3.1%) (20) b (4.0%) 155 (3.3%) Retired 1258 (29.5%) 90 (18.2%) 1348 (28.3%) Household work (unpaid) 181 (4.2%) (25) b (5.1%) 206 (4.3%) Unable 162 (3.8%) (27) b (5.5%) 189 (4.0%) Partner cohabitation Yes 2767 (64.8%) 280 (56.7%) 3047 (64.0%) No 1500 (35.2%) 214 (43.3%) 1714 (36.0%) Experienced limitation Severely limited 320 (7.5%) (44) (8.9%) 364 (7.6%) Mildly limited 1339 (31.4%) 137 (27.7%) 1476 (31.0%) Not limited 2608 (61.1%) 313 (63.4%) 2921 (61.4%) a Multiple categories were displayed collapsed when cell sizes <20 observations to avoid re-identifiability according to EHIS anonymisation rules. b Cells containing between 20 and 49 observations are individually flagged according to EHIS anonymisation rules.
|
PMC11699213_p30
|
PMC11699213
|
Descriptive statistics of the sample
| 4.022606 |
biomedical
|
Study
|
[
0.9976499676704407,
0.0009239542414434254,
0.0014261096948757768
] |
[
0.9995941519737244,
0.00020176800899207592,
0.0001532530295662582,
0.00005087511453893967
] |
en
| 0.999998 |
When considering social capital, the highest prevalence of never attending BCS was among those with no social network (13.35%), those with little perceived social support (13.36%), and those who find it very difficult to get help from neighbours (16.25%).
|
PMC11699213_p31
|
PMC11699213
|
Descriptive statistics of the sample
| 1.847996 |
biomedical
|
Study
|
[
0.8445001840591431,
0.0037311671767383814,
0.15176858007907867
] |
[
0.9617258906364441,
0.03727707639336586,
0.0005963981384411454,
0.0004006493545603007
] |
en
| 0.999998 |
Single women (16.49%) showed the highest rates of never attending BCS among all marital statuses. Women living with a partner and children (16.51%), women unable to work (13.76%), unemployed women (13.68%), and women not cohabiting with a partner (12.49%) displayed the highest prevalences. Lastly, severely limited women (12.09%) had the lowest attendance rates among their PROGRESS-Plus dimension.
|
PMC11699213_p32
|
PMC11699213
|
Descriptive statistics of the sample
| 1.916037 |
biomedical
|
Study
|
[
0.8346059918403625,
0.0027878584805876017,
0.1626061648130417
] |
[
0.9833764433860779,
0.016077745705842972,
0.0003311636100988835,
0.000214670566492714
] |
en
| 0.999998 |
Based on a recent scoping review , four PROGRESS-Plus variables were relevant for predicting lifetime BCS attendance: migration background, income, urbanisation degree and partnership cohabitation .
|
PMC11699213_p33
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 1.860916 |
biomedical
|
Review
|
[
0.7655478715896606,
0.004816614091396332,
0.22963544726371765
] |
[
0.2238718718290329,
0.07781852781772614,
0.6961659789085388,
0.0021436724346131086
] |
en
| 0.999995 |
Univariate logistic regression analyses separately estimated the effects of these four variables ( Table 3 ). Only cohabitation significantly predicted BCS attendance, with women living alone having higher odds of never attending. Age also had a significant relationship with BCS attendance. Table 3 Univariate logistic regression on never attending BCS in Germany. Table 3 Sociodemographic variables OR 95% CI R 2 model AUC model Income High 1 Low 1.20 (0.98–1.47) 0.0010 0.5187 Country of origin Germany 1 Not Germany 0.74 (0.50–1.11) 0.0007 0.5090 Degree of urbanisation Urban 1 Rural 0.87 (0.72–1.05) 0.0007 0.5170 Partner cohabitation Yes 1 No 1.41∗∗∗ (1.17–1.70) 0.0039 0.5408 Age 50–54 1 55–59 0.44∗∗∗ (0.35–0.56) 60–64 0.37∗∗∗ (0.28–0.47) 65–69 0.31∗∗∗ (0.23–0.40) 0.0317 0.6225
|
PMC11699213_p34
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 4.072528 |
biomedical
|
Study
|
[
0.99875807762146,
0.0005880504031665623,
0.0006538303568959236
] |
[
0.9994789958000183,
0.00020839153148699552,
0.0002540854038670659,
0.00005854920163983479
] |
en
| 0.999995 |
Multivariate logistic regression was performed to capture the effects of each predictor when adjusting for covariates and age ( Table 4 ). Here, the only relationship that showed a statistically significant relationship with BCS attendance was partner cohabitation, with 1.45 higher odds (p < 0.001) for women not cohabitating with their partners. Table 4 Multivariate logistic regression on never attending BCS in Germany (main effects model). Table 4 Sociodemographic variables OR 95% CI Income High 1 Low 1.21 (0.98–1.49) Country of origin Germany 1 Not Germany 0.68 (0.46–1.02) Degree of urbanisation Urban 1 Rural 0.91 (0.75–1.11) Partner cohabitation Yes 1 No 1.45∗∗∗∗ (1.19–1.76) Age 50–54 1 55–59 0.43 ∗∗∗ (0.34–0.56) 60–64 0.36 ∗∗∗ (0.28–0.47) 65–69 0.29 ∗∗∗ (0.22–0.38) R 2 0.0394 AUC-ROC 0.6539 A complete case analysis only based on the variables would have resulted in 300 more participants, but the results do not change meaningfully – see Appendix C .
|
PMC11699213_p35
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 4.107833 |
biomedical
|
Study
|
[
0.9989368319511414,
0.0004893385921604931,
0.0005738186882808805
] |
[
0.999546229839325,
0.00018014578381553292,
0.00022041340707801282,
0.00005320612035575323
] |
en
| 0.999997 |
Sixteen intersectional groups were created based on the combination of the four variables identified in the literature. Fig. 1 depicts the size and prevalence of each group. Fig. 1 Prevalence and size across the sixteen evidence-informed intersectional groups. a Cells containing between 20 and 49 observations are individually flagged according to EHIS anonymisation rules. Fig. 1
|
PMC11699213_p36
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 1.979732 |
biomedical
|
Study
|
[
0.9674137234687805,
0.001758516184054315,
0.03082774206995964
] |
[
0.973055899143219,
0.025725645944476128,
0.0007089631399139762,
0.0005094856023788452
] |
en
| 0.999998 |
Following this, an unweighted logistic regression was performed . As a reference group, we chose the one expected to have the highest attendance rate - based on the multivariate regression and Pedrós Barnils et al. - high-income women born outside Germany, living in urban areas with a partner (HOUY). Table 5 Full cross-classified multivariate logistic regression with evidence-informed intersectional groups. Table 5 OR 95% CI Intersectional groups HOUY 1 HGUY 2.49 (0.88–7.04) HGUN 2.97∗ (1.04–8.45) HGRY 1.92 (0.69–5.40) HGRN 2.84 (0.99–8.14) LGUY 1.96 (0.62–6.18) LGUN 3.71∗ (1.27–10.89) LGRY 2.81 (0.98–8.08) LGRN 3.24∗ (1.11–9.47) HOUN 3.11 (0.85–11.39) HORY 0.74 (0.16–3.46) HORN 3.15 (0.64–15.48) LOUY 0.66 (0.07–6.26) LOUN 4.00 (0.91–17.49) LORY 0.52 (0.05–4.82) LORN 9.48∗∗ (2.24–40.10) Age 50–54 1 55–59 0.43∗∗∗ (0.33–0.54) 60–64 0.35∗∗∗ (0.27–0.45) 65–69 0.29∗∗∗ (0.22–0.38) R 2 0.0445 AUC-ROC 0.6618 ∗p-value <0.05; ∗∗p-value <0.01; ∗∗∗ p-value <0.001. HGUY - high-income, born in Germany, urban, cohabitation. HGUN - high-income, born in Germany, urban, no cohabitation. HGRY - high-income, born in Germany, rural, cohabitation. HGRN - high-income, born in Germany, rural, no cohabitation. LGUY - low-income, born in Germany, urban, with cohabitation. LGUN - low-income, born in Germany, urban, no cohabitation. LGRY - low-income, born in Germany, rural, cohabitation. LGRN - low-income, born in Germany, rural, no cohabitation. HOUY - high-income, born outside Germany, urban, cohabitation. HOUN - high-income, born outside Germany, urban, no cohabitation. HORY - high-income, born outside Germany, rural, cohabitation. HORN - high-income, born outside Germany, rural, no cohabitation. LOUY - low-income, born outside Germany, urban, cohabitation. LOUN - low-income, born outside Germany, urban, no cohabitation. LORY - low-income, born outside Germany, rural, cohabitation. LORN - low-income, born outside Germany, rural, no cohabitation. Fig. 2 Odds Ratio (OR) with evidence-informed intersectional groups on never attending BCS in Germany. Fig. 2
|
PMC11699213_p37
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 4.097666 |
biomedical
|
Study
|
[
0.9931782484054565,
0.0003724902926478535,
0.006449262145906687
] |
[
0.9994305968284607,
0.000340945553034544,
0.00019566668197512627,
0.00003277223731856793
] |
en
| 0.999998 |
Four intersectional groups were significantly associated with never attending BCS. Low income women not born in Germany and living in rural areas with no partner (LORN) showed the highest odds (OR = 9.48, p = 0.002). The confidence intervals for all these estimations were rather wide, increasing the uncertainty of the predicted estimations. The DA of the full cross-classification model was moderated and 0.0079 points higher than the main effects model. That indicates that the regression with intersectional groups discriminates slightly better between women attending or never attending BCS than the main effects model.
|
PMC11699213_p38
|
PMC11699213
|
Analytical strategy a: evidence-informed regression
| 3.160758 |
biomedical
|
Study
|
[
0.9887905120849609,
0.0009302208200097084,
0.01027924008667469
] |
[
0.999163031578064,
0.0005487273447215557,
0.0002273454301757738,
0.00006089474481996149
] |
en
| 0.999997 |
Out of the three algorithms, CART showed the highest sensitivity and balanced accuracy performance. For more information on the hypertuned models, see Appendix D . The inner performance (i.e. evaluated on trained data) of CART was: 72.47% sensitivity, 51.35% specificity, 61.91% balanced accuracy, 14.71% positive predictive value and 94.15% negative predictive value. The moderate sensitivity suggests reasonable confidence in CART detecting women not attending BCS. However, the low specificity suggests small confidence in CART to identify negative cases (i.e. women attending BCS). The small positive predictive value indicates that many cases classified as positive (i.e. not attending BCS) are false positives. Nevertheless, the high negative predictive value indicates very few false negatives and, therefore, very high confidence that those cases classified as negative are negative (i.e. not assuming that a woman is attending BCS when she is not). Fig. 3 and Table 6 show the final decision tree and the emerged intersectional groups. Fig. 3 CART decision tree on never attending BCS in Germany. Fig. 3 Table 6 Intersectional groups on never attending BCS in Germany based on CART. Table 6 Group Intersectional groups Rank a Size, Prevalence H Women living with a partner, retired or doing unpaid household work 1 N = 882 Pr = 0.0454 E Widowed women living alone, with children, with a partner and children or other arrangements, residing in Baden-Württemberg, Berlin, Hesse, Mecklenburg-Vorpommern, Lower Saxony, North Rhine-Westphalia, Rhineland-Palatinate, Saxony, Saxony-Anhalt, and Schleswig-Holstein or Thuringia 2 N = 316 Pr = 0.0506 C Single, married or divorced women living in other living arrangements, with some or no perceived social support 3 N = 211 Pr = 0.0616 G Women living with a partner, who are either employed, unemployed, unable to work, or in other categories, and residing in Baden-Württemberg, Brandenburg, Hesse, Mecklenburg-Vorpommern, Lower Saxony, North Rhine-Westphalia, Rhineland-Palatinate, Saxony, Saxony-Anhalt, or Schleswig-Holstein 4 N = 918 Pr = 0.0730 B Single, married or divorced women living alone, with children, with a partner and children, with some or no perceived social support 5 N = 953 Pr = 0.1301 F Women living with a partner who are either employed, unemployed, unable to work, or in other working categories and residing in Bavaria, Berlin, Bremen, Hamburg, Saarland or Thuringia 6 N = 472 Pr = 0.1377 D Widowed women living alone, with children, with a partner and children or other arrangements, residing in Bavaria, Brandenburg, Bremen, Hamburg, or Saarland 7 N = 136 Pr = 0.1471 A Single, married or divorced women living alone, with children, with a partner and children or other arrangements, with little, uncertain or a lot of perceived social support 8 N = 873 Pr = 0.1707
|
PMC11699213_p39
|
PMC11699213
|
Analytical strategy b: decision tree-based regression
| 4.125537 |
biomedical
|
Study
|
[
0.9971966743469238,
0.00045815607882104814,
0.002345192711800337
] |
[
0.9995241165161133,
0.00021487977937795222,
0.0002258570893900469,
0.000035153308999724686
] |
en
| 0.999996 |
CART identified household type, marital status, working situation, region and perceived social support as relevant variables. The first splitting point, the root node, is the household type, where women living with a partner are split from all other household types. Women living with a partner are further split into working situations. Here, women retired or doing unpaid household work form a final node , and women employed, unemployed, unable to work, or others further split based on their region .
|
PMC11699213_p40
|
PMC11699213
|
Analytical strategy b: decision tree-based regression
| 1.643796 |
other
|
Study
|
[
0.12354346364736557,
0.0006838637054897845,
0.8757727146148682
] |
[
0.6818530559539795,
0.31597834825515747,
0.001298646442592144,
0.0008699563331902027
] |
en
| 0.999997 |
Women living alone, with children, with a partner and children or in other arrangements are further split based on marital status. Here, widowed women are separated from single, married or divorced women. Widowed women were lastly split based on their region . On the other hand, single, married or divorced women further split based on their perceived social support. Those with some or no perceived social support are separated from those with little, uncertain or a lot of perceived social support, who form a final node . The first group split one last time based again on their type of household: living alone, with children, or with a partner and children , and other arrangements .
|
PMC11699213_p41
|
PMC11699213
|
Analytical strategy b: decision tree-based regression
| 1.173654 |
other
|
Other
|
[
0.012052912265062332,
0.0005274161230772734,
0.9874196648597717
] |
[
0.022759027779102325,
0.9760245680809021,
0.0005937468959018588,
0.0006226907134987414
] |
en
| 0.999996 |
An unweighted logistic regression with CART intersectional groups adjusted by age was carried out using the group with the lowest never-attended BCS prevalence (Group H) as the reference category . Table 7 Multivariate logistic regression with CART intersectional groups on never attending BCS in Germany. Table 7 OR 95% CI CART intersectional groups A 3.02∗∗∗ (2.02–4.50) B 2.18∗∗∗ (1.45–3.27) C 1.12 (0.58–2.18) D 3.43∗∗∗ (1.91–6.10) E 1.05 (0.57–1.90) F 2.54∗∗∗ (1.62–3.98) G 1.26 (0.81–1.96) H 1 Age 50–54 1 55–59 0.49∗∗∗ (0.37–0.62) 60–64 0.46∗∗∗ (0.35–0.60) 65–69 0.44∗∗∗ (0.32–0.60) R 2 0.0534 AUC-ROC 0.6726 Fig. 4 Odds Ratios (OR) from CART intersectional groups on never attending BCS in Germany. Fig. 4
|
PMC11699213_p42
|
PMC11699213
|
Analytical strategy b: decision tree-based regression
| 4.048069 |
biomedical
|
Study
|
[
0.9990105628967285,
0.0004100124060641974,
0.000579334853682667
] |
[
0.999484658241272,
0.0002897529338952154,
0.00017395915347151458,
0.00005158810017746873
] |
en
| 0.999997 |
After adjusting by age, four CART intersectional groups showed a statistically significant difference compared to group H. Group D showed the highest odds of never attending BCS (OR = 3.43; p < 0.001), and Group E the lowest odds (OR = 1.05; p = 0.88).
|
PMC11699213_p43
|
PMC11699213
|
Analytical strategy b: decision tree-based regression
| 3.513365 |
biomedical
|
Study
|
[
0.9983629584312439,
0.0006057395366951823,
0.0010311988880857825
] |
[
0.9991382360458374,
0.0005778679042123258,
0.00021728486171923578,
0.000066619053541217
] |
en
| 0.999996 |
The total DA of the model CART was moderate . This value is 0.0108 points higher than the evidence-informed regression, indicating better discriminatory accuracy than the evidence-informed approach.
|
PMC11699213_p44
|
PMC11699213
|
Analytical strategy b: decision tree-based regression
| 2.678982 |
biomedical
|
Study
|
[
0.9825642704963684,
0.0007097640773281455,
0.016725938767194748
] |
[
0.9923571348190308,
0.006717368494719267,
0.0007775247213430703,
0.00014789968554396182
] |
en
| 0.999997 |
This study aimed to identify intersectional groups of women aged 50–69 at higher risk of never attending BCS in Germany comparing two different analytical strategies: evidence-informed regression and decision tree-based regression.
|
PMC11699213_p45
|
PMC11699213
|
Summary of findings
| 3.118178 |
biomedical
|
Study
|
[
0.9923183917999268,
0.0012959777377545834,
0.00638557830825448
] |
[
0.9991742968559265,
0.0005347077385522425,
0.0002009699965128675,
0.00008998130942927673
] |
en
| 0.999997 |
The evidence-informed approach identified low-income women who were not born in Germany, residing in rural areas and are not cohabitating with their partner as those at the highest risk of never attending BCS. In contrast, the decision tree-based approach yielded additional insights regarding specific regions of residence and family status. In this regard, the highest-risk group comprised women living alone, with children, with a partner and children, or in other arrangements, residing in Bavaria, Brandenburg, Bremen, Hamburg, or Saarland.
|
PMC11699213_p46
|
PMC11699213
|
Summary of findings
| 2.38502 |
biomedical
|
Study
|
[
0.9514285922050476,
0.0025231295730918646,
0.04604829102754593
] |
[
0.9876633286476135,
0.011617103591561317,
0.0005174970719963312,
0.0002020640386035666
] |
en
| 0.999997 |
The evidence-informed intersectional group matrix presented low-income women not born in Germany living in rural areas and cohabiting with a partner as those with the lowest prevalence of never attending BCS and low-income women not born in Germany living in rural areas and not cohabiting with a partner as those with the highest prevalence. These two groups differ solely on partnership cohabitation, illustrating a classical intersectional hypothesis: the contingency of inequities, whereby discrimination experienced in a specific social position depends on its interactions with other social positions. Cohabitation with a partner acts as a determining factor for low-income women not born in Germany and living in rural areas on their likelihood of attending BCS.
|
PMC11699213_p47
|
PMC11699213
|
Summary of findings
| 2.364452 |
biomedical
|
Study
|
[
0.6189320087432861,
0.0015139341121539474,
0.37955406308174133
] |
[
0.9829850792884827,
0.016255071386694908,
0.0005789948627352715,
0.00018090555386152118
] |
en
| 0.999997 |
The decision tree-based approach also identified household type as a relevant variable, revealing that women living with a partner generally had a lower risk of never attending BCS than those in other living arrangements. Several authors have previously conveyed the importance of partnership cohabitation and breast cancer screening attendance . Furthermore, living arrangements seem to play a role for those with some or no social support. The risk of never attending BCS was found to be half that of women with some or no social support who were living in other arrangements, compared to those living alone, with children, or with a partner and children. Furthermore, the role of perceived social support in the intersectional group identified as relevant (i.e. single, married or divorced women living alone, with children, with a partner and children or in other arrangements) is unclear, as previously noted in the literature .
|
PMC11699213_p48
|
PMC11699213
|
Summary of findings
| 3.914144 |
biomedical
|
Study
|
[
0.9980138540267944,
0.000434015499195084,
0.0015521412715315819
] |
[
0.9993434548377991,
0.000233254351769574,
0.00038499649963341653,
0.0000382187390641775
] |
en
| 0.999998 |
Lastly, the decision tree split by federal states twice in its third node. In both splits, women residing in Bavaria, Bremen, Hamburg, and Saarland indicate a higher risk of never attending BCS. These four federal states have been identified in other studies as having lower BCS attendance after invitation , reinforcing the higher compliance with preventive behaviours in former East Germany compared to the West. The use of the decision tree facilitated the identification of regional disparities among specific intersectional subgroups of women that would otherwise have remained unnoticed.
|
PMC11699213_p49
|
PMC11699213
|
Summary of findings
| 2.242736 |
biomedical
|
Study
|
[
0.9591030478477478,
0.002187639009207487,
0.03870930150151253
] |
[
0.987481415271759,
0.011888658627867699,
0.0004219218099024147,
0.0002081055281450972
] |
en
| 0.999998 |
The interpretability of the decision tree-based regression was slightly enhanced compared to the cross-classified regressions since it entailed fewer intersectional groups. Moreover, this reduction in dimensions did not entail a loss of information. On the contrary, the discriminatory accuracy of the model was slightly higher than the evidence-informed regression. Furthermore, the confidence intervals of the decision tree-based regression estimations are reduced (i.e. smaller variance), suggesting a more precise estimation of effect sizes.
|
PMC11699213_p50
|
PMC11699213
|
Comparison of regression- and decision tree-based approaches
| 3.802737 |
biomedical
|
Study
|
[
0.9728603363037109,
0.00040625937981531024,
0.026733428239822388
] |
[
0.9980564713478088,
0.0013182746479287744,
0.0005747966351918876,
0.00005047187369200401
] |
en
| 0.999996 |
Nevertheless, in this study no clearly discernible pattern of inequalities emerged among the PROGRESS-Plus characteristics, strengthening the heterogeneous findings reported by Pedrós Barnils et al. . Additional variables beyond the categorisation of sociodemographic factors, such as process-oriented variables (e.g. unpaid household work), could be explored to assess their relationship with BCS attendance.
|
PMC11699213_p51
|
PMC11699213
|
Comparison of regression- and decision tree-based approaches
| 2.384738 |
biomedical
|
Study
|
[
0.9114983081817627,
0.0015915317926555872,
0.0869101881980896
] |
[
0.9981052875518799,
0.0014319930924102664,
0.0003667297132778913,
0.00009596853487892076
] |
en
| 0.999998 |
This article does not aim to defend the use of any approach over another. As the “no-free-lunch theorem” in the machine learning literature often states, no single model works best in all scenarios . Nonetheless, this article encourages peer colleagues to evaluate different analytical strategies to answer their research question, while being aware of the advantages and disadvantages offered by each approach.
|
PMC11699213_p52
|
PMC11699213
|
Comparison of regression- and decision tree-based approaches
| 1.087767 |
other
|
Other
|
[
0.015067452564835548,
0.0008287517703138292,
0.984103798866272
] |
[
0.006342136766761541,
0.9850812554359436,
0.007642955984920263,
0.0009337280644103885
] |
en
| 0.999997 |
The evidence-informed approach synthesises existing research to identify variables that are relevant to BCS attendance, making a normative decision on which axis of inequality to explore. However, using repeatedly explored social dimensions may result in the stigmatisation of certain collectives and the under-exploration of others . Moreover, recommendations can only be formulated based on analysed social dimensions. Consequently, a potentially biased selection of variables can result in biased recommendations for developing interventions.
|
PMC11699213_p53
|
PMC11699213
|
Comparison of regression- and decision tree-based approaches
| 2.572257 |
biomedical
|
Study
|
[
0.6933797597885132,
0.0023504719138145447,
0.30426982045173645
] |
[
0.49869948625564575,
0.3335849344730377,
0.1664932519197464,
0.0012223434168845415
] |
en
| 0.999998 |
Conversely, the decision tree-based approach uses statistical algorithms to inductively identify patterns and relationships from the dataset . This approach is advantageous in revealing combinations of social dimensions not previously identified or explored, enabling more targeted interventions. Nevertheless, decision trees are susceptible to data quality, and their hierarchical structure might produce spurious results (i.e. the initial split has a significant impact on subsequent splits) . Moreover, given the uncommon application of decision trees in public health, no standardised procedures are yet defined, hence, many decisions are left to the discretion of the researcher (i.e. researcher bias).
|
PMC11699213_p54
|
PMC11699213
|
Comparison of regression- and decision tree-based approaches
| 3.785449 |
biomedical
|
Study
|
[
0.9788313508033752,
0.00037783311563543975,
0.02079089730978012
] |
[
0.7424733638763428,
0.1732640564441681,
0.08395785093307495,
0.0003047054633498192
] |
en
| 0.999998 |
From a quantitative intersectionality perspective, the decision tree approach yields certain advantages for answering the question , “Who is at higher risk of never attending BCS?“ . The regression with a full cross-classification based on evidence-identified variables inevitably results in a loss of information due to the category simplification required to build the matrix . To maintain cells with sufficient size to preserve statistical power, variable categories are dichotomised, compromising the possibility of identifying non-linear patterns amid these categories . Decision trees allow for high dimensionality in the included variables (i.e. without risk of multicollinearity) and their categories (i.e. no need for dichotomisation).
|
PMC11699213_p55
|
PMC11699213
|
Comparison of regression- and decision tree-based approaches
| 3.984281 |
biomedical
|
Study
|
[
0.9946216344833374,
0.00045754422899335623,
0.0049208588898181915
] |
[
0.9566686153411865,
0.03733144327998161,
0.005856249015778303,
0.0001437255705241114
] |
en
| 0.999997 |
To the authors’ knowledge, this is the first study to compare regression- and decision tree-based approaches for identifying intersectional subgroups of women at higher risk of not attending BCS. However, the study is not without limitations, in particular the cross-sectional design of the survey, which impedes any causal inference from being drawn, and its self-report methodology, which may introduce response bias. The response rate for EHIS wave 3 in Germany was 21.6%, highlighting the necessity for caution when interpreting findings from studies utilising this dataset. Lastly, previous studies have indicated that EHIS may underestimate disparities in access to screening programmes .
|
PMC11699213_p56
|
PMC11699213
|
Strengths and limitations
| 4.012106 |
biomedical
|
Study
|
[
0.9986467957496643,
0.00041968078585341573,
0.0009335623471997678
] |
[
0.9994992017745972,
0.00014438264770433307,
0.00031183083774521947,
0.000044643784349318594
] |
en
| 0.999994 |
The combination of regression and decision tree-based approaches provides a comprehensive strategy for identifying intersectional groups at higher risk of an outcome. In this study, the evidence-informed regression identified that low-income women who were not born in Germany lived in rural areas, and did not cohabit with their partner as being at the highest risk of never attending BCS. Conversely, the decision tree-based approach identified widowed women living alone, with children, with a partner and children, or in other arrangements, and residing in specific federal states (i.e. Bavaria, Brandenburg, Bremen, Hamburg, or Saarland) as the highest risk group. The decision tree-based approach slightly outperformed the regression-based approach in its overall performance and interpretability and added a nuanced, data-driven layer of analysis, that enhances the overall understanding of the PROGRESS-Plus characteristics that determine BCS attendance in Germany.
|
PMC11699213_p57
|
PMC11699213
|
Conclusion
| 4.042078 |
biomedical
|
Study
|
[
0.996553897857666,
0.00048646473442204297,
0.0029596337117254734
] |
[
0.9995100498199463,
0.00021952748647890985,
0.00023081619292497635,
0.00003956663204007782
] |
en
| 0.999998 |
Núria Pedrós Barnils: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Benjamin Schüz: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.
|
PMC11699213_p58
|
PMC11699213
|
CRediT authorship contribution statement
| 1.015102 |
other
|
Other
|
[
0.2108144611120224,
0.0036658900789916515,
0.7855195999145508
] |
[
0.0040707094594836235,
0.9952027797698975,
0.0003297788498457521,
0.00039674583240412176
] |
en
| 0.999996 |
This study does not require ethical approval as it is a secondary analysis of de-identified data. Access to the data was granted by Eurostat, the European body for Statistics, and all authors have signed the individual confidentiality declaration following Regulation (EC) No 223/2009 .
|
PMC11699213_p59
|
PMC11699213
|
Ethical statement
| 1.223872 |
other
|
Other
|
[
0.19817285239696503,
0.0013423878699541092,
0.800484836101532
] |
[
0.03658560663461685,
0.9619284868240356,
0.0009215557365678251,
0.0005642788601107895
] |
en
| 0.999995 |
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Open-access funding is provided by Bremen University.
|
PMC11699213_p60
|
PMC11699213
|
Financial disclosure statement
| 1.009605 |
other
|
Other
|
[
0.0030835603829473257,
0.0008347620023414493,
0.9960816502571106
] |
[
0.0011923053534701467,
0.9976997971534729,
0.000563483452424407,
0.000544403272215277
] |
en
| 0.999996 |
The results and conclusions are mine and not those of Eurostat, the European Commission or any of the national statistical authorities whose data have been used.
|
PMC11699213_p61
|
PMC11699213
|
Declaration of interest statement
| 0.956482 |
other
|
Other
|
[
0.01699971780180931,
0.000831819255836308,
0.9821684956550598
] |
[
0.0017427224665880203,
0.9974912405014038,
0.0004271875659469515,
0.00033884451841004193
] |
en
| 0.999998 |
Periodontal regenerative treatment aims to provide suitable conditions for periodontal regeneration. Gingival recession can compromise esthetics and lead to root surface caries and tooth hypersensitivity. Several techniques have been suggested for root coverage, including pedicle flap, free gingival graft, guided tissue regeneration, and allografts. Autogenous grafts are procured from the palate or alveolar ridge and have limitations such as donor site morbidity and limited availability.
|
PMC11699267_p0
|
PMC11699267
|
Introduction
| 3.789307 |
biomedical
|
Review
|
[
0.9975047707557678,
0.0016546796541661024,
0.0008405885891988873
] |
[
0.03643285483121872,
0.2678121328353882,
0.6936163902282715,
0.002138603711500764
] |
en
| 0.999995 |
Acellular dermal matrix (ADM) is an alternative to autogenous grafts. ADM has applications for root coverage, augmentation of keratinized tissue around teeth and implants, and treatment of gingival recession. 1 It eliminates the need for autogenous grafts and subsequent pain and discomfort. However, the absence of vasculature and cells in ADM slows down the unity and blending of the graft with the host tissue compared to autogenous grafts. Also, allograft requires cell attachment and anastomosis of the vasculature for maturity and reorganization. 2
|
PMC11699267_p1
|
PMC11699267
|
Introduction
| 3.936985 |
biomedical
|
Other
|
[
0.9980194568634033,
0.0012608828255906701,
0.0007196437800303102
] |
[
0.07749850302934647,
0.7973253130912781,
0.12336254864931107,
0.0018136061262339354
] |
en
| 0.999996 |
Tissue engineering enables the fabrication of structures with the desired shape using biomaterials and progenitor cells and also allows cell proliferation and differentiation on suitable scaffolds. 3 ADM also serves as a temporary matrix for tissue regeneration, enhances the adhesion and proliferation of cells, and plays a key role in the transfer of MSCs to the defect site. 4
|
PMC11699267_p2
|
PMC11699267
|
Introduction
| 3.836317 |
biomedical
|
Other
|
[
0.9992467164993286,
0.000408502877689898,
0.00034484313800930977
] |
[
0.32231810688972473,
0.6240103840827942,
0.05253336951136589,
0.0011381518561393023
] |
en
| 0.999998 |
An ideal scaffold must be biocompatible, easy to use, and easily fixed at the site. Also, it should have interconnected porosities to allow the growth and proliferation of mesenchymal stem cells (MSCs) and angiogenesis. Moreover, it should have osteoconductive and osteoinductive properties. 5
|
PMC11699267_p3
|
PMC11699267
|
Introduction
| 3.726474 |
biomedical
|
Other
|
[
0.9959624409675598,
0.002412439091131091,
0.001625189557671547
] |
[
0.022024594247341156,
0.9725142121315002,
0.004817677196115255,
0.0006435454706661403
] |
en
| 0.999996 |
The strength and stability of the scaffold also play an important role in the proliferation and differentiation of MSCs. 6 The size of porosities in the scaffold also affects the attachment, proliferation, and differentiation of MSCs. 7 Large pores provide less surface for the attachment of cells, and numerous pores increase the number of attachments. 8
|
PMC11699267_p4
|
PMC11699267
|
Introduction
| 3.90413 |
biomedical
|
Study
|
[
0.9992828965187073,
0.00012602563947439194,
0.0005910461768507957
] |
[
0.9536870121955872,
0.03957590088248253,
0.006542871706187725,
0.00019417896692175418
] |
en
| 0.999997 |
MSCs are commonly used for cell therapy and tissue engineering due to their self-renewal property and differentiation ability. 9 These cells can be isolated from different human tissues. 10
|
PMC11699267_p5
|
PMC11699267
|
Introduction
| 3.097522 |
biomedical
|
Other
|
[
0.9984670281410217,
0.00046994705917313695,
0.0010629729367792606
] |
[
0.10236331075429916,
0.8644030690193176,
0.03214508667588234,
0.0010885873343795538
] |
en
| 0.999997 |
Many studies are available on MSCs’ attachment, proliferation, and morphology on different commercially available scaffolds; however, studies on the MSCs’ behavior on ADMs produced in Iran are scarce. This study compared the biological behavior of MSCs on two types of commercial ADM scaffolds commonly used for root coverage.
|
PMC11699267_p6
|
PMC11699267
|
Introduction
| 2.94794 |
biomedical
|
Study
|
[
0.9981308579444885,
0.0004887334653176367,
0.001380453584715724
] |
[
0.9976691603660583,
0.0016707684844732285,
0.0005098027759231627,
0.00015018435078673065
] |
en
| 0.999998 |
The present in vitro study was conducted on two types of ADM scaffolds, namely scaffold type I (CenoDerm®, Tissue Regeneration Corporation, Tehran, Iran) and scaffold type II (Acellular Dermis, Iranian Tissue Product Co., Tehran, Iran). Of each scaffold, 26 samples were evaluated in this study. Also, 26 empty wells served as controls 11 (78 samples). One of the samples in each group was used to evaluate the morphologic characteristics of the cells. The scaffolds were coded to blind the operator to the group allocation of scaffolds.
|
PMC11699267_p7
|
PMC11699267
|
Methods
| 4.013183 |
biomedical
|
Study
|
[
0.9992801547050476,
0.000370234833098948,
0.0003496409044601023
] |
[
0.9994984865188599,
0.0002502558345440775,
0.00019054405856877565,
0.00006067295907996595
] |
en
| 0.999999 |
MSCs isolated from a sample of the buccal fat pad were seeded and cultured. The tissue specimens were immersed in sterile phosphate-buffered saline (PBS) (Sigma, USA) supplemented with 100-U/mL penicillin (Sigma, USA), 100-μg/mL streptomycin (Sigma, USA), and 2-mg/mL collagenase type IV (Sigma, USA) and incubated at 37°C for 90 minutes. After filtering the cell suspension using a 70-μm filter (SPL, Korea), they were cultured in a 75-cm 2 cell culture flask (SPL, Korea) containing alpha modification of Eagle’s medium (SPL, Korea) supplemented with 100-μg/mL streptomycin, 15% fetal bovine serum (SPL, Korea), 100-U/mL penicillin, 200-mM L-glutamine and 100-mM ascorbic acid 2-phosphate (Sigma, USA). The cells were incubated with 5% CO 2 and 95% air at 37 °C for 24 hours. After this period, unattached cells were rinsed off with PBS. The medium was refreshed every three days.
|
PMC11699267_p8
|
PMC11699267
|
Cell isolation and culture
| 4.156729 |
biomedical
|
Study
|
[
0.9992169141769409,
0.0005643015028908849,
0.00021875850507058203
] |
[
0.9957465529441833,
0.0034622936509549618,
0.0005918003735132515,
0.0001993257028516382
] |
en
| 0.999996 |
Twenty-six rectangular pieces from each scaffold group, measuring 1.5 × 1 cm, with 0.2‒0.6 mm thickness, were rinsed with sterile saline solution (SPL, Korea) in 500-mL flasks for 10 minutes according to the manufacturer’s instructions. The samples were adapted to the bottom of 52 wells in six plates (SPL, Korea). Scaffolds I and II were placed in five wells on each of the five plates. Five empty wells were also considered as the control group in each plate. The sixth plate containing one sample of each scaffold and one empty well as control was used to assess cell morphology. The cell suspension with a density of 16,000 cells/mL was added to the scaffolds and control wells and incubated at 37 °C and 5% CO 2 for 12, 24, and 84 hours and 7 days. In total, two plates were used for cell attachment assessment using 6,4-diamidino-2-phenylindole (DAPI) staining and methyl thiazole tetrazolium (MTT) assay at 12 hours, and 3 plates were used to assess cell proliferation using the MTT assay at 24 and 84 hours and 7 days. 12 Five replicates were performed in every assessment at each time interval. One plate was used for cell morphology assessment at 24 hours.
|
PMC11699267_p9
|
PMC11699267
|
Preparation of scaffold and cell seeding
| 4.142394 |
biomedical
|
Study
|
[
0.9993199110031128,
0.0004259888955857605,
0.00025406412896700203
] |
[
0.999311089515686,
0.0003208076814189553,
0.00030426253215409815,
0.00006385802407748997
] |
en
| 0.999997 |
DAPI staining: The cell fixation was performed by 12 hours of incubation with 2.5% glutaraldehyde (Sigma, USA) and stained with 50 μg/mL of DAPI stain (Sigma, USA) for 30 minutes. The samples were washed with PBS to eliminate unattached cells. Then, the cells were observed under a fluorescence microscope at a 290 nm wavelength and counted in five points (four points at the corners and one at the center). 12 This was repeated for five samples in all the three groups.
|
PMC11699267_p10
|
PMC11699267
|
Assessment of cell attachment
| 4.041369 |
biomedical
|
Study
|
[
0.9994725584983826,
0.00025628178264014423,
0.00027116163983009756
] |
[
0.9924038052558899,
0.007001185789704323,
0.0004361185128800571,
0.00015887296467553824
] |
en
| 0.999996 |
MTT assay: Optical density (OD) was measured 12 hours after culture to determine the primary attached cells in all the groups with five repetitions. 12
|
PMC11699267_p11
|
PMC11699267
|
Assessment of cell attachment
| 3.568965 |
biomedical
|
Study
|
[
0.9990981817245483,
0.00027714608586393297,
0.0006247275741770864
] |
[
0.9804051518440247,
0.018861660733819008,
0.0005156135885044932,
0.00021752624888904393
] |
en
| 0.999998 |
The cell viability and proliferation on the scaffolds and the control group were assessed 24 and 84 hours and 7 days after culture using the MTT assay. In this way, 200 μL of RPM1640 and 20 μL of fresh MTT solution (5 mg/mL) (Sigma, USA) were added to the cell culture wells, followed by incubation at 37 °C under 5% CO 2 for 4 hours. 12 Tetrazolium salt present in MTT was absorbed by biologically active cells, resulting in formation of purple formazan crystals, which were dissolved by adding isopropanol (Sigma, USA), including 0.1-N HCL (150 mL/well). The OD of the solution was read by a microplate spectrophotometer (SPL, Korea) by decreasing the wavelength from OD690 to OD570. 13 For assessment of cell proliferation and attachment, cell viabilityat each time interval was performed separately for five samples of each group, and determined based on a linear diagram representing the correlation between OD and cell number.
|
PMC11699267_p12
|
PMC11699267
|
Assessment of cell viability and proliferation
| 4.129219 |
biomedical
|
Study
|
[
0.9994786381721497,
0.0003038034774363041,
0.00021755027410108596
] |
[
0.9992969036102295,
0.00026328317471779883,
0.0003791821363847703,
0.000060647882492048666
] |
en
| 0.999997 |
To assess cell morphology, the cells were cultured on scaffolds and a control group and incubated for 24 hours. Then, they were washed twice with PBS, fixed with 2.5% glutaraldehyde for one hour at room temperature, and dehydrated with six graded concentrations of ethanol (from 50% to 100%), and hexamethyldisilazane (Sigma, USA). The samples were then gold-coated and evaluated under a scanning electron microscope (SEM; Nikon, Japan) at × 1000 magnification. One sample of each group was scanned under the SEM. Two parameters were assessed, including scaffold surface area covered with cells (in square micrometers) and roundness of the cells (smaller-to-larger diameter ratio of the cells). 14 Cell morphology assessment was performed on one sample of each group.
|
PMC11699267_p13
|
PMC11699267
|
Assessment of cell morphology
| 4.114707 |
biomedical
|
Study
|
[
0.9994862079620361,
0.00028362084412947297,
0.00023023229732643813
] |
[
0.9994038343429565,
0.00028053144342266023,
0.0002621819730848074,
0.000053463423682842404
] |
en
| 0.999995 |
Cell proliferation and attachment experiments were performed in five replicates. All the results were statistically analyzed using SPSS 25 (SPSS Inc., USA). Means ± standard deviations were used for adhesion and proliferation data analysis. The Mann-Whitney test was used to statistically analyze cell attachment, while ANOVA was applied to assess the proliferation of cells. Post hoc Tukey tests were applied for pairwise comparisons in cases of significant differences. A P value of < 0.05 with a 95% confidence interval was considered statistically significant.
|
PMC11699267_p14
|
PMC11699267
|
Statistical analysis
| 3.945659 |
biomedical
|
Study
|
[
0.9995737671852112,
0.00016981248336378485,
0.00025646714493632317
] |
[
0.9992139339447021,
0.00043755953083746135,
0.0002948195324279368,
0.000053676187235396355
] |
en
| 0.999995 |
Cell attachment was assessed in 30 samples using the MTT assay and DAPI staining (five samples from each group for each test) at 12 hours.
|
PMC11699267_p15
|
PMC11699267
|
Results
| 3.644028 |
biomedical
|
Study
|
[
0.999030351638794,
0.00032257454586215317,
0.000647028093226254
] |
[
0.9977970123291016,
0.0017891458701342344,
0.0003214189491700381,
0.00009248509013559669
] |
en
| 0.999995 |
In the MTT assay, the scaffold II group had the highest cell attachment, followed by the control group. ANOVA showed no significant difference in cell attachment between the three groups ( P = 0.4).
|
PMC11699267_p16
|
PMC11699267
|
Results
| 3.841119 |
biomedical
|
Study
|
[
0.9990529417991638,
0.0004048433620482683,
0.0005422424292191863
] |
[
0.99920254945755,
0.0004485371755436063,
0.00028400690644048154,
0.00006494174886029214
] |
en
| 0.999997 |
In DAPI staining , the highest attachment was noted in scaffold I, followed by the control group. ANOVA showed that the difference between the three groups was not significant ( P = 0.4) ( Table 1 ).
|
PMC11699267_p17
|
PMC11699267
|
Results
| 3.754638 |
biomedical
|
Study
|
[
0.9987897276878357,
0.00037907445221208036,
0.0008311408455483615
] |
[
0.9992502331733704,
0.0004630912153515965,
0.0002265579387312755,
0.000060138227127026767
] |
en
| 0.999997 |
In the assessment of cell proliferation, 45 samples were evaluated at 24 and 84 hours and 7 days in all the groups (5 samples in each group) with MTT assay ( Table 2 ).
|
PMC11699267_p18
|
PMC11699267
|
Results
| 3.904759 |
biomedical
|
Study
|
[
0.9992085099220276,
0.000340953964041546,
0.0004506558470893651
] |
[
0.9993115663528442,
0.0004096461634617299,
0.00021342748368624598,
0.00006532116822199896
] |
en
| 0.999996 |
Over time, cell proliferation increased in all the three groups. At 24 hours, the highest proliferation rate was noted in the scaffold II group, followed by scaffold I. ANOVA showed that the proliferation rate was significantly higher in scaffolds I and II groups compared to the control group ( P < 0.001). Pairwise comparisons by Tukey’s test showed that the difference between the two scaffolds was not significant ( P = 0.8).
|
PMC11699267_p19
|
PMC11699267
|
Results
| 4.099633 |
biomedical
|
Study
|
[
0.9993948936462402,
0.0002954804222099483,
0.0003096498840022832
] |
[
0.9994662404060364,
0.00016749386850278825,
0.0003201095969416201,
0.000046096152800600976
] |
en
| 0.999997 |
At 84 hours, the highest proliferation rate was noted in the control group, followed by the scaffold I group. ANOVA showed no significant difference in this regard between the control and scaffold groups ( P = 0.2) or between the two scaffold groups ( P = 0.9).
|
PMC11699267_p20
|
PMC11699267
|
Results
| 3.946061 |
biomedical
|
Study
|
[
0.9990116357803345,
0.00038619397673755884,
0.0006020793225616217
] |
[
0.999417781829834,
0.0003015298570971936,
0.00022882592747919261,
0.00005189525836613029
] |
en
| 0.999997 |
At seven days, the highest proliferation rate was noted in the control group, followed by the scaffold I group. The difference in this regard between the three groups was statistically significant ( P = 0.01).
|
PMC11699267_p21
|
PMC11699267
|
Results
| 3.79918 |
biomedical
|
Study
|
[
0.9986090064048767,
0.0004568859003484249,
0.0009341437253169715
] |
[
0.9992050528526306,
0.0003924913762602955,
0.0003417992847971618,
0.00006066838977858424
] |
en
| 0.999996 |
In all the three groups, the proliferation rate increased over time (shown by the MTT assay) such that in the control group, multiple comparisons revealed significant differences in the proliferation rate over time ( P < 0.001).
|
PMC11699267_p22
|
PMC11699267
|
Results
| 3.897155 |
biomedical
|
Study
|
[
0.999208390712738,
0.0002911959309130907,
0.0005004042759537697
] |
[
0.9991981387138367,
0.0004149807500652969,
0.0003313264169264585,
0.00005554706513066776
] |
en
| 0.999997 |
In scaffold I, ANOVA showed that the difference in the proliferation rate was statistically significant over time ( P = 0.01). In scaffold II, ANOVA showed that the proliferation rate difference was not significant over time ( P = 0.2).
|
PMC11699267_p23
|
PMC11699267
|
Results
| 3.260912 |
biomedical
|
Study
|
[
0.9977060556411743,
0.00039004962309263647,
0.00190390192437917
] |
[
0.9983901977539062,
0.0012251805746927857,
0.0003087676886934787,
0.00007595183706143871
] |
en
| 0.999998 |
In the three groups, the highest proliferation rate was noted at 84 hours and 7 days (the highest cell count was noted in the control group, with the lowest in the scaffold II group).
|
PMC11699267_p24
|
PMC11699267
|
Results
| 3.572888 |
biomedical
|
Study
|
[
0.9984706044197083,
0.0005059647955931723,
0.0010233721695840359
] |
[
0.9984999895095825,
0.001203207764774561,
0.00022336463734973222,
0.00007341260061366484
] |
en
| 0.999997 |
Figure 2 shows the OD of MSCs of all the groups at all time intervals. Assessment of cell morphology under SEM at 24 hours revealed greater cell expansion with more appendages in the control group, followed by the scaffold I group compared to the scaffold II group .
|
PMC11699267_p25
|
PMC11699267
|
Results
| 3.918003 |
biomedical
|
Study
|
[
0.9992214441299438,
0.00032558038947172463,
0.0004530429723672569
] |
[
0.9990529417991638,
0.0006457653944380581,
0.00023314941790886223,
0.00006804396980442107
] |
en
| 0.999997 |
In this study, two commonly used scaffolds, CenoDerm and Acellular Dermis, were used. Attachment (at 12 hours) and proliferation (at 24 and 84 hours and 7 days) of MSCs cultured on these scaffolds were assessed using the MTT assay plus DAPI staining and MTT assay, respectively. Morphological properties of cells were also evaluated under SEM at 24 hours. The results showed no significant difference between these scaffolds concerning cell attachment at 12 hours. However, better results were achieved with CenoDerm at 24 hours and 7 days concerning cell morphological properties and cell proliferation, respectively.
|
PMC11699267_p26
|
PMC11699267
|
Discussion
| 4.082512 |
biomedical
|
Study
|
[
0.9993352293968201,
0.00035675131948664784,
0.0003079684101976454
] |
[
0.9994556307792664,
0.00013339772704057395,
0.0003547967644408345,
0.00005615999907604419
] |
en
| 0.999998 |
Cell attachment is the first response of the cell to scaffold. 15 Primary cell attachment to scaffold depends on the size and amount of porosities, water, and protein absorption 16 and plays an essential role in the proliferation of cells.
|
PMC11699267_p27
|
PMC11699267
|
Discussion
| 3.348478 |
biomedical
|
Other
|
[
0.9968506693840027,
0.00048195375711657107,
0.0026674000546336174
] |
[
0.24942125380039215,
0.7457610964775085,
0.004266211297363043,
0.0005514168296940625
] |
en
| 0.999998 |
According to Pabst et al, 17 an autologous scaffold enhancing proliferation of human gingival fibroblasts, endothelial cells, osteoblasts, and oral keratinocytes in vitro can also show higher angiogenic properties in vivo. Thus, scaffolds might show more favorable behaviors in vitro and have higher applicability in vivo.
|
PMC11699267_p28
|
PMC11699267
|
Discussion
| 3.837275 |
biomedical
|
Study
|
[
0.9996067881584167,
0.00012872542720288038,
0.00026450149016454816
] |
[
0.982951819896698,
0.00468220142647624,
0.012208848260343075,
0.00015711419109720737
] |
en
| 0.999994 |
In the present study, the MTT assay showed no significant difference at 12 hours in cell attachment between the three groups. Similarly, Ma et al 18 used an MTT assay and showed proper attachment of fibroblasts to bilayer dermal equivalent. They indicated that using bilayer dermal equivalent also resulted in optimal regeneration in vivo. Thus, the results of MTT can be generalized to the clinical setting.
|
PMC11699267_p29
|
PMC11699267
|
Discussion
| 4.022439 |
biomedical
|
Study
|
[
0.9995306730270386,
0.00022108173288870603,
0.000248331663897261
] |
[
0.9993683695793152,
0.00022654753411188722,
0.0003521653125062585,
0.0000529788012499921
] |
en
| 0.999998 |
Hussein et al 19 also assessed the attachment of fibroblasts to scaffolds with different sterilization methods using the MTT assay and DAPI staining, with both tests showing similar results. Thus, DAPI staining can confirm the results of the MTT assay in vitro. Also, the results of DAPI staining in vitro can be generalized to the clinical setting. In the present study, to confirm the results of the MTT assay, DAPI staining was also performed after 12 hours, which showed the same results, and both showed no significant difference between the three groups in cell attachment. Regarding the current study analysis, it might be concluded that the attachment of cells was the same in two types of scaffolds. Thus, they probably have the same efficacy for use in the clinical setting regarding attachment of MSCs.
|
PMC11699267_p30
|
PMC11699267
|
Discussion
| 4.054839 |
biomedical
|
Study
|
[
0.9995591044425964,
0.0002316631143912673,
0.00020922752446494997
] |
[
0.9992050528526306,
0.00018317786452826113,
0.0005592294619418681,
0.00005257983139017597
] |
en
| 0.999997 |
SEM showed morphological differences, demonstrating the superiority of scaffold I to scaffold II, which indicates the more biologically active cells. 20 Greater expansion of cells in the control group might be due to the smoother surface of wells in the control group compared to the porous surface of scaffolds. 21
|
PMC11699267_p31
|
PMC11699267
|
Discussion
| 3.34229 |
biomedical
|
Study
|
[
0.9987108707427979,
0.00026903057005256414,
0.0010200109099969268
] |
[
0.9923978447914124,
0.00706181675195694,
0.00038718784344382584,
0.00015308705042116344
] |
en
| 0.999996 |
The current study assessed the proliferation of MSCs in the three groups after 24 and 84 hours and 7 days using the MTT assay. It showed that the proliferation rate significantly increased over time, which was not significant in scaffold II between time intervals. At 24 or 84 hours, there were no significant differences between the scaffold groups in this respect. At 7 days, the significantly lowest cell population rate was noted in the scaffold II group. Overall, cell proliferation in the scaffold I group was higher than in the scaffold II group. The higher proliferation rate in the control group at each time interval might be attributed to the smooth surface of the plate compared to the porous surface of scaffolds. As previously confirmed, surface topography affects the attachment and differentiation of cells. 22 Osteoblasts have a greater attachment to rougher surfaces, while fibroblasts and MSCs better adhere to smoother surfaces. 23 - 25
|
PMC11699267_p32
|
PMC11699267
|
Discussion
| 4.091872 |
biomedical
|
Study
|
[
0.9994390606880188,
0.00032331800321117043,
0.00023755774600431323
] |
[
0.9993921518325806,
0.000150682230014354,
0.000398625765228644,
0.00005849461740581319
] |
en
| 0.999997 |
In assessing the proliferation rate and morphological properties, scaffold I showed better results than scaffold II. Thus, it might also be superior for clinical use because evidence shows that the results of the MTT assay can be generalized to the clinical setting.
|
PMC11699267_p33
|
PMC11699267
|
Discussion
| 2.94198 |
biomedical
|
Study
|
[
0.9969823956489563,
0.0005460905376821756,
0.0024714984465390444
] |
[
0.9603953957557678,
0.036787744611501694,
0.0023770362604409456,
0.0004398360615596175
] |
en
| 0.999996 |
We assessed MSC morphology, attachment, and proliferation, which are essential parameters in wound healing and repair. Using both DAPI and MTT assays simultaneously was a strength of our study. Also, no previous study has compared these two scaffolds concerning MSC behavior. The study was performed blindly, and each measurement was repeated five times.
|
PMC11699267_p34
|
PMC11699267
|
Discussion
| 3.486014 |
biomedical
|
Study
|
[
0.9992719292640686,
0.00028400495648384094,
0.00044410748523660004
] |
[
0.9990848302841187,
0.000603556982241571,
0.00024564514751546085,
0.00006586848030565307
] |
en
| 0.999997 |
Hydrophilicity, pore size, biocompatibility, mechanical properties, composition, and solvent or toxic compounds in the scaffold all affect cell seeding. 13 The structure of biomaterials in the cellular matrix is also important and affects cell behaviors such as attachment, proliferation, and differentiation. 26 Attachment and proliferation of cells on scaffolds depend on the availability of nutrients, porosity, and interconnection between pores. 27 The Strength and density of the scaffold also affect cell morphology. 28 According to the above, further studies are recommended to compare these scaffold structural properties and the efficacy of these scaffolds in MSCs’ behavior in clinical situations.
|
PMC11699267_p35
|
PMC11699267
|
Discussion
| 3.989626 |
biomedical
|
Study
|
[
0.9994497895240784,
0.00020350365957710892,
0.00034676151699386537
] |
[
0.917864203453064,
0.0019728890620172024,
0.0799747034907341,
0.00018826057203114033
] |
en
| 0.999996 |
Both scaffolds showed similar efficacy in attachment of MSCs in vitro, but the proliferation of MSCs after 7 days was higher on scaffold I compared to scaffold II. Also, MSCs on scaffold I were more active, expanded more, and had more cellular appendages. Scaffold I was superior to scaffold II in terms of proliferation and morphology of MSCs in vitro.
|
PMC11699267_p36
|
PMC11699267
|
Conclusion
| 4.099189 |
biomedical
|
Study
|
[
0.9995044469833374,
0.00027176496223546565,
0.00022374185209628195
] |
[
0.9990591406822205,
0.0003652879677247256,
0.0005155947292223573,
0.00005996781328576617
] |
en
| 0.999998 |
As the authors of this paper, we thank the Nanotechnology Department of Tehran University of Medical Sciences for allowing us to conduct our experiments in their laboratory. We also thank Amirkabir University for SEM analyses.
|
PMC11699267_p37
|
PMC11699267
|
Acknowledgments
| 0.943028 |
other
|
Other
|
[
0.2988608479499817,
0.0026344938669353724,
0.698504626750946
] |
[
0.00647776247933507,
0.992519736289978,
0.0005242910701781511,
0.00047820358304306865
] |
en
| 0.999997 |
The authors declare no competing interests.
|
PMC11699267_p38
|
PMC11699267
|
Competing Interests
| 0.878638 |
other
|
Other
|
[
0.027219654992222786,
0.0024039065465331078,
0.9703763723373413
] |
[
0.007326771505177021,
0.9895817041397095,
0.0017047872534021735,
0.0013866664376109838
] |
en
| 0.999995 |
Not applicable.
|
PMC11699267_p39
|
PMC11699267
|
Consent for Publication
| 1.026613 |
other
|
Other
|
[
0.3120197653770447,
0.00332370912656188,
0.68465656042099
] |
[
0.018966801464557648,
0.9788615703582764,
0.0013643786078318954,
0.0008072974742390215
] |
fr
| 0.713643 |
All data from the current study are available upon request from the corresponding author.
|
PMC11699267_p40
|
PMC11699267
|
Data Availability Statement
| 0.884684 |
biomedical
|
Other
|
[
0.5905567407608032,
0.003785520326346159,
0.40565773844718933
] |
[
0.0396096408367157,
0.9578003287315369,
0.0015875640092417598,
0.001002542907372117
] |
en
| 0.999996 |
Not applicable. The study was conducted in vitro without human participation. Therefore, the study did not have any ethical registrations.
|
PMC11699267_p41
|
PMC11699267
|
Ethical Approval
| 1.315083 |
biomedical
|
Other
|
[
0.9442352652549744,
0.001483928062953055,
0.054280851036310196
] |
[
0.0883905291557312,
0.9085643887519836,
0.0019154141191393137,
0.0011296633165329695
] |
en
| 0.999998 |
Adult WT C57BL6 mice used in this study were housed in animal house with a facility providing individual ventilated cages with ad libitum access to food and water throughout the study. All the animals used in the study were male and aged between 6 and 8 weeks at the start of the high-fat diet (HFD). The animal house maintained a 12-12 h light and day cycle with an ambient temperature of 22–25°C with relative humidity controlled. All animal procedures used in this study were approved by the Institutional Animal Ethics Committee of IIT, KGP. A total of 16 animals were used for the study, and all were fed an HFD containing 1.25% cholesterol for 20 weeks. The animals were randomly divided into two groups: one continued with HFD and the other with HFD + atorvastatin (ATS; 10 mg/kg). The statin was carefully measured and added to the animals' drinking water for 10 weeks. Animals were sacrificed, and the blood was collected by cardiac puncture for serum collection. The organs were harvested, immediately frozen using liquid nitrogen in Dewar flasks, and stored at −80°C until further molecular analysis.
|
39566851_p0
|
39566851
|
Animals and treatment
| 4.053996 |
biomedical
|
Study
|
[
0.9994415640830994,
0.00036345611442811787,
0.00019495950255077332
] |
[
0.9993510842323303,
0.0003589337575249374,
0.00021953383111394942,
0.00007042082870611921
] |
en
| 0.999997 |
Subsets and Splits
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Returns a sample of 100 clinical case documents, providing a basic overview of the document type's content.