Title and abstract screening for systematic reviews with Jev, a System One model: comparison with generative large language models

Abstract

Large language models (LLMs) screen titles and abstracts without review-specific training, but generating screening decisions as text takes processing time and incurs API charges. We evaluated Jev, a non-generative model returning classification probabilities, on 4527 records from two systematic reviews of bipolar disorder treatments. We used human reviewers' decisions to retain records for full-text assessment as the reference standard. In the primary analysis, we asked Jev yes-or-no questions ("Noul") about whether to retain each record. Records were retained when their retention probability was at least 50%, a cutoff specified before the final evaluation runs. We also evaluated lower retention thresholds. For comparison, we gave Jev explicit include/exclude options ("Choice") and used the same prompts with GPT-5 mini and GPT-6 Astra; both LLMs used default reasoning. In the primary analysis at 50%, sensitivity and specificity were 99.6% and 98.9% for light therapy and 93.2% and 93.5% for adjunctive pharmacotherapy. Lower cutoffs selected on these data retained all reference-positive records. Compared with assessing every record manually, these cutoffs reduced the number requiring human assessment by 96.9% for light therapy and 61.3% for adjunctive pharmacotherapy. For light therapy and adjunctive pharmacotherapy, sensitivity was 92.3% and 90.0% with Jev using explicit options at 50%, 87.2% and 90.0% with GPT-5 mini, and 92.3% and 80.8% with GPT-6 Astra. Costs for screening all 4527 records once were US$0.20 for Jev using explicit options, US$2.59 for GPT-5 mini, and US$33.45 for GPT-6 Astra. Jev's probabilities could provide graded information for reviewers assessing titles and abstracts.