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The present study explores the discriminatory performance of existing and novel gold-standard-specific machine learning (GSS-ML) focused filter models (i.e., models built specifically for a retrieval task and a gold standard against which they are evaluated) and compares their performance to citation count and impact factors, and non-specific machine learning (NS-ML) models (i.e., models built for a different task and/or different gold standard).

A Comparison of Citation Metrics to Machine Learning Filters for the Identification of High Quality MEDLINE Documents