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Postprandial glucose response (PPGR) prediction is a central promise of personalized nutrition, yet published models are rarely evaluated against the reliability of the measurement they try to predict. We reproduce and extend the CGMacros baseline (45 subjects, 1,699 meals; leave-one-subject-out) and subject it to a cross-cohort stress test on BIG IDEAs (16 subjects, 656 meals). The replication matches the published breakfast result (iAUC r = 0.655 against approx. 0.64) and extends it to all analysable meals (iAUC 0.451; AUC 0.838). Duplicate reference breakfasts in CGMacros give a repeatability of rho = 0.799, a ceiling the model stays below (sqrt(rho) approx. 0.894) and so does the published baseline. A frozen model transfers forward at r = 0.227 for iAUC; trained on the external cohort and transferred back it reaches 0.085, so iAUC transfer fails in both directions. AUC behaves differently: it survives transfer (0.569 forward, 0.748 reverse) and is largely a baseline-glucose quantity - pre-prandial glucose alone recovers 0.807 of AUC across all meals, 0.861 at breakfast, and 0.963 at the person level. Read against its own regime, the within-cohort breakfast model captures 0.54 of the reliable variance, a same-device cross-population transfer in a prior study also 0.54, and the cross-device transfer 0.11. Cross-regime comparisons of PPGR models are therefore confounded by measurement reliability and cannot be read as comparisons of model quality: reliability explains part of the gap, and the remainder is attributable to features, device, annotation and model class in proportions this design cannot separate. The claim is conditional and falsifiable, and we state the study that would overturn it. We recommend that PPGR reports name their measurement regime, place a reliability estimate beside every correlation, and report per-subject alongside marginal interval coverage.