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Table 1 Review question

From: Automation of literature screening using machine learning in medical evidence synthesis: a diagnostic test accuracy systematic review protocol

Item Description
“Participants”* Original publications and literatures identified by electronic literature search
Index test Automatic literature screening models using artificial intelligence algorithms
Reference standard Traditional literature screening by human investigators
Outcome Primary outcome: diagnostic accuracy, measured by sensitivity, specificity, precision, NPV, PPV, NLR, PLR, DOR, F-measure, accuracy, and AUC of automatic literature screening models
Secondary outcomes: labour and time saving, mainly evaluated by the percentage of retrieved literatures that the reviewers do not have to read (because they have been screened out by the automatic literature screening models)
  1. Abbreviations: AUC, area under curve; DOR, diagnostic odds ratio; NLR, negative likelihood ratio; NPV, negative predictive value; PLR, positive likelihood ratio; PPV, positive predictive value
  2. *The “participants” in our review refer to the original publications and literatures identified in a systematic literature search, rather than human participants or patients in traditional systematic reviews