The Epistemological Fragility of Big Medical Data: How Automated Models Fail Clinical Reality

This note addresses a critical emerging threat to Global Health: the proliferation of unverified clinical signals derived from large-scale retrospective databases. By exposing our scientific experience with two high-profile cases, we demonstrate how sophisticated statistical modeling (e.g., Cox regression) can inadvertently crystallize epidemiological impossibilities when disconnected from clinical reality and national gold standards. This contribution argues for a systematic epistemological maintenance to safeguard the scientific record and prevent e-iatrogenesis in public health policy.