def predict_match_data(matcher, match_data):
    """
    Input: 
        matcher: A matcher object initialised
                on a corpus of references (e.g. all of EPMC)
        match_data: A pandas dataframe with the
                references you want to find title matches for
                in the corpus (e.g. references found in policy documents).
                This must have a column called 'Title'
                and two unique identifier columns 'Document id'
                and 'Reference id'.
    Output:
        predictions: A dataframe of the reference matches found using
                the matcher function. Each line will have 'Reference id'
                from the match_data references, and if it matched a 
                reference from the corpus references then the 'Matched publication id'
                of this reference will be given. If there was no match
                the Matched publication id column will be None. The 'Cosine Similarity'
                column gives the score of how similar the matched 
                reference titles are.
    """

    predictions = []
    for ref in match_data:
        matched_publications = matcher.match(ref)
        if matched_publications:
            predictions.append(matched_publications['Matched publication id'])
        else:
            predictions.append(None)

    return predictions