aceLightweight open-source reasoning large language model for evidence synthesis screening
Objectives: To evaluate performance and efficiency of a lightweight open-source reasoning large language model (LLM) against an expert-derived reference standard for screening scientific publication databases. Materials and methods: Two expert reviewers screened subsets of database records across 3 stages: (1) base eligibility (n = 200), (2) obesity focus (n = 200), and (3) women of reproductive age focus (n = 150). The LLM (gpt-oss-20b) screened all records (n = 20 118). Performance (F1-score and sensitivity [r…
A 20-billion-parameter open-source reasoning model screened database records against an expert reference standard with 90%-95% sensitivity at about 19.4 records per minute, versus roughly 1.0 for human reviewers.
Evidence
- Peer-reviewedJAMIA Open2026-10-23
How should this claim be treated?
Truvace Impact Record TRV-2026-2240, v1: “Lightweight open-source reasoning large language model for evidence synthesis screening.” Truvace, 2026-10-24. /record/TRV-2026-2240 (accessed at citation time). sha256 fbef38220ddbf43f…
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