aceMatching Surgical Datasets to Prediction Tasks: NSQIP and Non-NSQIP Artificial Intelligence Models Across Specialties
Background Surgical artificial intelligence models depend on training data that may not capture predictors required for specific outcomes. We compared discrimination of models derived from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) with models using non-NSQIP datasets across surgical specialties and prediction tasks. Study design Structured searches of PubMed, Scopus, and EMBASE identified adult surgical prediction models published from October 1, 2015, through January…
Across 295 surgical prediction models, those trained on the standardized NSQIP database had lower average discrimination than models from other datasets, and the gap depended on the outcome predicted.
The authors report that the differences between NSQIP and non-NSQIP models were outcome-dependent and sensitive to precision weighting, so the pooled gap does not hold uniformly across tasks.
Evidence
- Peer-reviewedJournal of the American College of Surgeons2026-10-01
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Truvace Impact Record TRV-2026-1489, v1: “Matching Surgical Datasets to Prediction Tasks: NSQIP and Non-NSQIP Artificial Intelligence Models Across Specialties.” Truvace, 2026-10-15. /record/TRV-2026-1489 (accessed at citation time). sha256 2c7c937e9e12eeea…
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