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Table 5 Semi-automation test with RobotAnalyst using a training set of dually reviewed citations from a review-of-review with labels from full-text screening

From: Comparison of a traditional systematic review approach with review-of-reviews and semi-automation as strategies to update the evidence

 

Traditional search

Total citations: 3181

Title-abstract screening: 201 includes/2980 excludes

Full-text screening: 59 includes/3122 excludes

 

Review of reviews

Total SR citations: 30

Total primary study citations: 95

Title-abstract screening: 76 includes/49 excludes

Full-text screening 33 includes/43 excludes

Number of citations not screened: 3030

Training set: ROR citations

Labeled citations: 125 (4%)

Training set labels: TP (33), FP (0), TN (92)

Unlabeled citations assigned inclusion prediction by ML algorithm: 3056

  

Inclusion prediction: 0.3

Inclusion prediction: 0.4

Inclusion Prediction: 0.5

Predicted includes

NA

3040

2819

1166

Predicted excludes

NA

16

237

1890

Sensitivity

54%

100%

97%

69%

Specificity

100%

3%

10%

63%

Missed citations

26

0

2

18

Burden

4%

99%

93%

41%

Time savings (min)

1561

8

118.5

945

  1. FP false positive, ML machine learning, NA not applicable, TP true positive, TN true positive