Artificial Intelligence Distributionally Robust Inference of Community Clinic Preeclampsia Blood-Pressure Prediction Under Unmeasured Cuff Size Mismatch: A Healthcare Worst-Case Calibration Evaluation

Cuff size mismatch is an unmeasured, non-ignorable source of error in community-clinic blood-pressure (BP) measurement, and it is precisely the kind of error that standard calibration diagnostics cannot see. No observational data are used or claimed: the analysis characterises the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.640), with power estimated across 2000 design cells at cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The framework formalises cuff-size mismatch as an ambiguity set over a bounded multiplicative perturbation of the latent BP signal, derives a worst-case risk functional, and shows that the robust predictor is a shrinkage estimator whose penalty is governed by the intraclass correlation. The design evaluation indicates that 54 clusters are required for 5 percentage-point effects at 80% power under the declared variance structure, and that minimum detectable effects fall steeply between 8 and 24 clusters before flattening. The contribution is a calibration protocol that replaces point-identified BP prediction with a worst-case bound, making explicit the mismatch burden that community-clinic AI systems currently absorb silently.