aceSystematic Bias in Comparative Evaluations of Machine Learning Versus Logistic Regression for Clinical Prediction Models: A Meta-Research Analysis Using Trauma Mortality as an Empirical Case
Objective Comparative evaluations of machine learning (ML) and logistic regression (LR) for clinical prediction frequently report ML as superior, but the methodological framework producing those comparisons has received limited scrutiny. We aimed to quantify the apparent discrimination advantage of ML over LR using trauma mortality prediction as an empirical case, and to characterise the evaluation practices that shape it. Study design and setting Systematic review and random-effects meta-analysis combined with…
Across 17 studies totaling 243,324 trauma patients, the best-performing ML model showed a small pooled AUC advantage over logistic regression for mortality prediction.
Apparent superiority of ML over logistic regression for trauma mortality prediction may be inflated by convergent practices including comparing best-of-several ML models to a single LR comparator, reliance on internal validation, selective reporting, and AUC-only synthesis.
Extreme between-study heterogeneity and wide prediction interval mean future studies could favor either approach, and most included studies had high or unclear risk of bias with internal validation only.
Evidence
- Peer-reviewedJournal of Clinical Epidemiology2026-09-11
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Truvace Impact Record TRV-2026-1068, v1: “Systematic Bias in Comparative Evaluations of Machine Learning Versus Logistic Regression for Clinical Prediction Models: A Meta-Research Analysis Using Trauma Mortality as an Empirical Case.” Truvace, 2026-09-13. /record/TRV-2026-1068 (accessed at citation time). sha256 16afba5e89a1c317…
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