Artificial Intelligence replication of Rural Clinic Blood Transfusion Matching Under Unmeasured Donor-Recipient Phenotype Misclassification: A Healthcare Robustness Evaluation

Blood transfusion matching in rural clinics depends on donor-recipient phenotype concordance, yet phenotype labels are frequently misclassified in ways that remain unmeasured in routine practice. the present analysis evaluates the robustness of an ordinary least squares estimator of matching outcomes when a binary group indicator absorbs unmeasured phenotype misclassification. A replication-based replication was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process with intercept 12.0, exposure 0.42, group -3.1, severity 1.35 and error SD 4.2, using seed 1268724284. The estimator was fitted to each replication and its sampling behaviour characterised. Estimated bias was -0.04079 for the intercept and 0.0003 for the exposure coefficient; root mean squared error was 0.83897 and 0.01508 respectively. Empirical coverage of nominal 95% confidence intervals was 1.0 for both parameters. The sampling distribution of the exposure coefficient was approximately normal, and the normal Q-Q plot was consistent with that approximation. The results indicate that under the stated process the estimator recovers the exposure coefficient with negligible bias and correct coverage, while the intercept absorbs the misclassification-affected group effect. Robustness in transfusion-matching replication depends less on the estimator than on whether the misclassified covariate is modelled as a group-level shift or as a measurement-error problem, and it derives the conditions under which the two diverge.