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  "path": "/t/a-longitudinal-renal-health-outcome-for-clinical-trials-in-acute-kidney-injury/28750#post_17",
  "publishedAt": "2026-05-22T12:52:50.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "David, if critical care scientists thought as deeply as you, we would not be in this state. I should have been more clear that the primary pathological step is the reification of simplistic ordinal composites as the presented renal score.\n\nS=1 ->Delta SOFA 2 ->{“sepsis”}\n\nThe score threshold is transformed into a presumed biological entity perceived as capable of generating a disease-level estimand, E2. This is the symbol–mechanism substitution fallacy. If a single courageous consensus-panel–dwelling critical care scientist fully understood the implications of that sentence, the entire epistemological foundation of modern syndrome-based critical care research would begin to unravel.\n\nOf course, once the gate is reified (which they have been for ~ 40 years) then:\n\n  * the synthetic category is treated as biologically coherent,\n\n  * the RCT estimand is treated as disease-specific,\n\n  * and E3 is mistaken for E2.\n\n\n\n\nThus, the symbol–mechanism substitution fallacy is essentially a modern causal-inference manifestation of the deeper philosophical error you describe: a symbolic abstraction, in this case, a synthetic data-generating process (SDGP) is mistaken for a coherent causal mechanism.\n\nCritical care medicine, despite possessing perhaps the richest physiologic information density in medicine, for ~4 decades has repeatedly collapses relational time-series into simplistic ordinal composites and then treats those abstractions as disease equivalents in the Bradford Hill sense. . The result is the repeated generation of unstable third-layer estimands masquerading as biologic knowledge.\n\nThe conflation of ARDS criteria with severe COVID pneumonia was perhaps the clearest recent example. An ordinal syndrome gate was substituted for an actual disease mechanism, and the resulting methodological error contributed to global harmful guideline extrapolations.\n\nIf the warnings and mathematical presentations made here in Datamethods had been understood and heeded that global error could have been prevented.\n\nSometimes I wonder if statisticians, trialists and guideline committees realize how close to the bedside they are. One never sees the type of deep, sometimes painful, intellectual introspection typical of a physician after treatment failure of a patient. Yet failure at the guideline level is profoundly more harmful but simply generates the “heterogeneous syndrome alibi” and no substantive methodological investigation. The cycle begins again. No one seems deeply saddened and intellectually stimulated and introspective as a physician would be.\n\nUnfortunately, the synthetic data-generating process enterprise is not easily subjected to any deep philosophical critique because entire research ecosystems, careers, and institutions are built upon it. As Upton Sinclair observed:\n\n“It is difficult to get a man to understand something, when his salary depends on his not understanding it.”\n\nThe offending ARDS guidelines were abandoned but nothing else of intellectual substance changed, so there is no reason why the same global error will not occur again because it is not acceptable to public ally debate the source of the error.\n\nWhen critical care science, which has the worst record of RCT based guideline reversals for harm of any field, is ready to openly debate E3, then we can wax philosophical. To me the creation of a new real ordinal composite was just an extension of Bone’s SIRS from 1987.",
  "title": "A Longitudinal Renal Health Outcome for Clinical Trials in Acute Kidney Injury?"
}