Artificial Intelligence replication of District Hospital Emergency Blood Gas Analyzer Calibration Scheduling Under Unmeasured Electrode Membrane Fouling: A Healthcare Measurement-Drift Robustness Evaluation

Blood gas analyzers in district hospital emergency departments are calibrated on fixed schedules, yet electrode membrane fouling accumulates unmeasured between services and biases the measured partial pressures and pH that clinicians use to guide resuscitation. Six hundred 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 1625439425. The estimator was characterised across replications. Estimated bias was -0.00363 for the intercept and 0.00017 for exposure; root mean squared error was 0.84178 and 0.01473 respectively; empirical coverage of the nominal 95% confidence intervals was 1.0 for both parameters. The results indicate that under the declared process the estimator is essentially unbiased and that nominal coverage is attained, which establishes a baseline against which fouling-induced drift can be quantified. Calibration scheduling should be evaluated by its robustness to unmeasured membrane fouling rather than by nominal calibration interval alone, and it specifies the replication architecture required to make that evaluation possible in district hospital settings.