Year: 2026
Type: preprint
Abstract: Title and abstract screening is among the most labor-intensive steps of systematic reviews. Jev, a low-cost AI decision model, scores records against eligibility criteria without screening labels. We scored 169,982 records from 128 reviews and, after tuning on development reviews, evaluated 23 held-out reviews once and 31 Cochrane reviews post hoc. Jev outranked the best free ranker in all 23 held-out reviews (mean AUC 0.966 vs 0.831) and came within 0.011 AUC of Claude Opus 5.5 on 1999 records at about 1/700 of its list-price cost (US$0.02/1000 records). Combined with active learning, it raised work saved at 95% recall from 0.659 to 0.806. A fixed score threshold fell below 95% recall in 3 of 28 Cochrane reviews, whereas Jev ranking with statistical stopping (post hoc) reached 95% recall in all 51 reviews, reading 86–88% of records. Label-free ranking with Jev was accurate and inexpensive, but stopping safely required screening decisions.
Source: medRxiv
Authors: Taeho Jo, Donghyun Lee, Leonard Lee
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7221543209