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Table 1 Definitions of commonly used terms

From: Assessing the accuracy of machine-assisted abstract screening with DistillerAI: a user study

Accuracy: the proportion of correctly classified records:
\( \frac{\left(\mathrm{TP}+\mathrm{TN}\right)}{\left(\mathrm{TP}+\mathrm{FP}+\mathrm{TN}+\mathrm{FN}\right)} \)
False negatives (FNs): the number of records incorrectly classified as excludes. Also referred to as “missed studies.”
False positives (FPs): the number of records incorrectly classified as includes.
Prediction: a forecast of whether a record is relevant (include) or irrelevant (exclude) for a given systematic review.
Semi-automated screening tool: any web-based application that employs a combination of text mining and text classification to assist systematic reviewers during the title and abstract screening process.
Sensitivity: the ability of a screening tool to correctly classify relevant records as includes: \( \frac{\mathrm{TP}}{\left(\mathrm{TP}+\mathrm{FN}\right)} \)
Specificity: the ability of a screening tool to correctly classify irrelevant records as excludes: \( \frac{\mathrm{TN}}{\left(\mathrm{TN}+\mathrm{FP}\right)} \)
Text classification: a standard machine-learning process in which the aim is to categorize texts into groups of interest [18].
Text mining: the process of discovering knowledge and structure from unstructured data.
True negatives (TNs): the number of records correctly identified as excludes.
True positives (TPs): the number of records correctly identified as includes.