ArticleJournal of the American Medical Informatics Association : JAMIA2026
Is one run enough? Reproducibility of flagship large language models across temperature and reasoning settings in biomedical text processing.
Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTo quantify run-to-run reproducibility of Gemini 3 Flash Preview and GPT-5.2 for trial-success classification across temperature and reasoning/thinking settings and determine whether single-run reporting suffices. MATERIALS AND
methodsWe utilized 250 trial abstracts labeled based on primary endpoint success. We evaluated Gemini across thinking levels (minimal, low, medium, high) and temperatures 0.0-2.0 and GPT-5.2 across reasoning-effort levels (none to x-high) with an additional temperature sweep when reasoning was disabled. Each setting was run 3 times.
resultsReproducibility was high for Gemini (κ = 0.942-1.000; invalid outputs 0%-1.5%) and GPT-5.2 (κ = 0.984-0.995; no invalid outputs). F1 remained stable (mean/majority vote 0.955-0.971), with marginal gains from majority voting.
conclusionFor binary biomedical classification with tightly constrained outputs, both models were reproducible across decoding and reasoning settings, suggesting single runs are often sufficient, with minimal replication as a practical stability check.
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