Table 7.

Sensitivity analysis of missing-label handling: macro-F1 and macro-AUC under the complete-case (Definition B), untested-as-negative (Definition A) and prevalence-based imputation definitions.

Label definition	Sample size	Train/Test	Macro-F1	Common Macro-F1	Macro-AUC
Definition A: all samples, “untested” coded as negative	24,689	19,751/4,938	0.1527	0.2292	0.7653
Definition B: complete-testing samples, authentic labels (primary)	21,395	17,116/4,279	0.1553	0.2202	0.7702
Imputation (n = 24,689)	24,689	19,751/4,938	0.1537	0.2184	0.7605
Difference (B − A)	—	—	+0.0026	−0.0090	+0.0049

The two label definitions shared identical feature engineering, algorithms, hyperparameters, random seeds and threshold-selection protocols; the only differences were label definition and sample inclusion. Imputation-based labels were drawn from a Bernoulli distribution with each pathogen’s cohort prevalence among the records with any untested pathogen (fixed seed); complete records kept their authentic labels. All three strategies shared identical features, algorithms, splits and threshold-selection protocols. All analyses used a fixed random seed of 42.