Artificial Intelligence Pediatric Malaria Severity Triage Under Variable Hemoglobinometer Cuvette Reuse: A Conformal Risk-Bounding Evaluation of Healthcare Referral Thresholds

Pediatric malaria triage in low-resource settings depends on hemoglobin measurements that are frequently obtained with reused hemoglobinometer cuvettes, a practice that introduces measurement error of uncertain magnitude into artificial-intelligence-based referral decisions. The framework treats the hemoglobin value as a noisy observation of an underlying true concentration, propagates a bounded measurement-error model through a triage classifier, and derives distribution-free guarantees on the false-referral and missed-severe-case rates. A before-after-control-impact design is characterised under a declared variance structure with between-cluster standard deviation of 6.0, residual standard deviation 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.56 clusters are required to detect a 5-percentage-point effect at 80% power. The contribution is a formal account of how measurement-error bounds translate into referral-threshold guarantees, and a demonstration that conformal risk control remains valid under cuvette-reuse distortion provided the error bound is correctly specified.