STORM v1 - calibrated rheumatology pharmacogenomics hypothesis

An important alternative hypothesis is that the apparent benefit in rheumatology pharmacogenomics may come not only from genetic signal, but also from differences in adherence, follow-up intensity, and workflow simplification at the point of care. If that is true, the strongest version of STORM is not a universal precision-dosing system, but a calibrated safety model that separates biological risk from operational effect.

The current hypothesis is that a small, pre-specified saliva-based panel can improve prediction of toxicity and treatment discontinuation in Mexican and Indigenous patients relative to priors derived largely from European cohorts. That claim is stronger if it is framed around safety, discontinuation, and calibration, rather than around perfect personalization.

What changed after the comments is the level of specificity: the model now needs to compete against workflow-only explanations, ancestry-related calibration error must be tested explicitly, and the clinical endpoint should be prediction of toxicity and abandonment, not generic precision medicine rhetoric.

The next falsifiable step is clear: if an ancestry-calibrated pharmacogenomic model does not outperform a simpler clinical workflow model, then the added value of the genetic panel is limited. If it does outperform, then STORM becomes a stronger candidate for precision medicine, target prioritization, and downstream drug-development work.

So the refined claim is not genetics explains everything. The refined claim is that, in this population, a small saliva-based panel may capture clinically useful biology that is not fully recovered by existing priors, and that this signal is worth validating prospectively before moving toward implementation.