ArticleApplied clinical informatics2026
Optimizing GPT-5 for Operation-Procedure-Code-Extraction from Operative Reports in Meningioma Surgery: Feasibility and Comparison of Context-Enhancements.
Article in Applied clinical informatics, 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
Objective: In our recent study we showed that GPT's ability to perform OPS-code extraction from operational reports was equivalent to coding neurosurgeons. In this study we aim to evaluate the effect of context enhancement on GPT-5's code extraction abilities. Methods: We provided OpenAIs GPT-5 with 100 operational reports (OR) of patients that underwent meningioma surgery. A chat prompt was generated instructing GPT to generate the correct OPS coding. Five groups were formed and provided with different context-enhancing modalities: 1. No additional context (GPT-5s), 2. the current OPS-catalogue (GPT-5o), 3. specified rules on Meningioma coding (GPT-5r), 4. code-triggering sample phrases based on 50 additional ORs (GPT-5e) and 5. all enhancements combined (GPT-5c). We analyzed code-extraction abilities, mistakes and hallucinations. Results: Non context enhanced GPT-5s showed the lowest rate of correct coding (44%) and highest number of hallucinations (105) and total mistakes (132). GPT-5o showed identical accuracy to GPT-5s (44%, Conclusion: We show, that specific context enhancement significantly improves GPT-5s ability to perform OPS-code extraction from operational reports, while significantly lowering mistake- and hallucination rates.
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