Key result
Evaluating AI chest pain triage under ECG lead reversal requires 52 clusters for adequate power.
Why the study?
Electrocardiograph lead reversal is a common, frequently undetected error in community clinic chest pain triage, and its interaction with artificial-intelligence-based decision support had not been characterised as a safety-guarantee problem.
What are the formal conditions and study design parameters required to evaluate whether artificial intelligence conformal triage guarantees survive unmeasured electrocardiograph lead reversal in community clinic chest pain presentations?
What are the formal conditions and study design parameters required to evaluate whether artificial intelligence conformal triage guarantees survive unmeasured electrocardiograph lead reversal in community clinic chest pain presentations?
This methodological study establishes the formal conditions under which AI conformal triage guarantees survive undetected ECG lead reversal and demonstrates that 52 clusters are needed for 80% power to detect a 5-percentage-point safety guarantee failure.
AI ECG triage demands cluster designs powered to detect lead-reversal safety failures; leaves open prospective validation of minimal standards.
Electrocardiograph lead reversal is a common, frequently undetected pre-analytical error in community clinic chest pain triage, and its interaction with artificial-intelligence-based decision support has not been characterised as a safety-guarantee problem. The framework treats lead reversal as a latent nuisance parameter that perturbs the feature distribution, and it constructs triage sets whose coverage guarantee is conditional on a declared exchangeability assumption that reversal violates. A before-after-control-impact design was used to characterise the operating characteristics of the evaluation under a declared variance structure with between-cluster SD of 6.0, residual SD of 4.5 and intraclass correlation of 0.64; power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 52 clusters are required for 80% power at a 5-percentage-point effect. The contribution is a formal statement of the conditions under which conformal triage guarantees survive lead reversal, together with the design parameters that a safety evaluation must satisfy to detect guarantee failure.
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Baldé et al. (2026) studied Community clinic chest pain presentations. Artificial intelligence conformalised triage under unmeasured ECG lead reversal was evaluated on Statistical power to detect conformal triage guarantee failure in a before-after-control-impact evaluation. AI conformalised chest pain triage evaluation under unmeasured ECG lead reversal requires 52 clusters to achieve 80% power for a 5-percentage-point effect (ICC 0.64).
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