The C-index illusion: discrimination without calibration in published survival models

"Stop Chasing the C-index when Evaluating Survival Analysis Models" (ICML 2026, Spotlight) argued normatively, on synthetic data, that evaluating survival models by discrimination alone, i.e. the concordance index, produces systematically misleading model comparisons, because the metric ignores calibration and time-dependent accuracy. Whether this matters for real, published, non-clinical models has not been tested. We reproduce three published survival-ML models across three structurally distinct domains (hard-drive failure, peer-to-peer credit default, and user disengagement on digital platforms), validate our evaluation instrument against the anchor paper's own synthetic experiment, and test five pre-registered hypotheses under a Holm-corrected family-wise error rate. Three of five reject. A model that reproduces the published discrimination almost exactly (C = 0.9595 vs. 0.958 reported) fails a formal calibration test at p = 2.6e-136; a broad feature-ablation search finds no single attribute responsible for this discrimination, so the calibration failure is not an artifact of a trivial shortcut. A lender's estimated default risk is biased upward by roughly two percentage points, growing to nearly four points in the riskiest segment, when loan prepayment is treated as non-informative censoring rather than as a competing risk. A platform's churn model shows probability estimates that degrade with the prediction horizon even as its global discrimination stays within the pre-registered C-index band. A direct test of whether metric choice inverts which model is preferred does not reject, though with limited power given only two to three models per domain; the failure mode we document is better characterized as misplaced confidence in a chosen model than as choice of the wrong one. We release a reusable, pre-registered evaluation harness with full code and a single annotated notebook.