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TRV-2026-0308Certified recordPeer-reviewed

Custom GPT models for complex rheumatology systematic reviews: A two-part evaluation of data extraction and prognosis appraisal

Background: Systematic reviews are essential for evidence-based practice but remain resource-intensive, particularly during full-text data extraction and structured risk-of-bias appraisal in prognostic research. These challenges are amplified in complex autoimmune diseases such as systemic lupus erythematosus (SLE). Recent advances in large language models (LLMs) have raised interest in their potential; however, rigorous benchmarking against expert reviewers in real-world rheumatology settings is limited. Object…

Health · The Trace — both readings · certified 2026-07-20 · v1 · article view · machine-readable

Current reading — gain

Custom GPT models completed all QUIPS domain judgments and reduced data-extraction time from 30.4 to 5.7 minutes per study in rheumatology systematic reviews.

Current reading — problem

GPT-Reviewer showed near-zero agreement with human QUIPS ratings for study participation and outcome measurement, with kappa 0.001.

What this doesn’t fix

Model performance was limited by poor handling of tables/supplements and need for domain-specific calibration before routine use in complex rheumatology synthesis.

Evidence

Reader signal

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Truvace Impact Record TRV-2026-0308, v1: “Custom GPT models for complex rheumatology systematic reviews: A two-part evaluation of data extraction and prognosis appraisal.” Truvace, 2026-07-20. /record/TRV-2026-0308 (accessed at citation time). sha256 c9d2ac77e870379e…

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