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Table 3 Descriptive characteristics of the title and abstract screening processes in Abstrackr, across three trials

From: Technology-assisted title and abstract screening for systematic reviews: a retrospective evaluation of the Abstrackr machine learning tool

Characteristic Topic
Antipsychotics
N = 12,763 records
Bronchiolitis
N = 5893 records
Child Health SRs
N = 5243 records
Diabetes
N = 47,385 recordsa
Screened by humanb
 N records 277 (32) 607 (340) 210 (10) 323 (206)
 % records 2.2 (0.3) 10.3 (5.8) 4.0 (0.2) 0.7 (0.4)
Accepted by humanc
 N records 19 (3) 56 (35) 118 (20) 111 (74)
 % records 6.9 (1.1) 9.0 (0.9) 56.1 (6.9) 34.1 (1.6)
Predicted as relevant by Abstrackrd
 N records 4259 (1281) 1163 (123) 4535 (173) 5187 (1430)
 % records 34.1 (10.2) 22.0 (0.9) 90.1 (3.6) 11.0 (3.0)
  1. All values are mean (SD) across three trials. Standard deviations for proportions (% records) relate to the range of values observed across trials, and not the mean variance across trials
  2. SR systematic review
  3. aIncluded some duplicates as three EndNote libraries were combined to create the dataset
  4. bBefore Abstrackr produced predictions
  5. cBased on the decisions of two independent human reviewers for each screening project
  6. dRecords that Abstrackr predicted as relevant for further inspection following title and abstract screening (equivalent to “accepted as relevant”)