The rapid transition toward large-scale retrospective Big Data analysis in biomedicine has introduced a critical epistemological paradox: a scenario where statistical precision often acts as a sophisticated mask for a profound lack of clinical accuracy. This Technical Note addresses the rising phenomenon of e-iatrogenesis, defined here as the systematic generation of unintended medical misinformation through the interpretation of unstable bio-data. We argue that sophisticated statistical models, when disconnected from national gold biomedical standards, can inadvertently crystallize epidemiological impossibilities. By examining three exemplar cases, spanning oncology, dermatology, and psychiatry, this work introduces a formal sensitivity stress test, the Sensitivity Hazard Ratio. This bio-computational metric is designed to measure signal instability by benchmarking big data-driven outcomes against national census-level health registries. Our analysis reveals that in all three instances, the reported clinical signals, such as a 77% reduction in schizophrenia or a fourfold increase in vitiligo incidence, were mathematically manufactured by failing denominators or structural selection biases within the data-driven cohorts. When recalibrated against authoritative national benchmarks, these perceived risks effectively dissolve, exposing the thin red line between a genuine epidemiological discovery and a simole computational outcome. We conclude that there is an urgent need for systematic epistemological maintenance of the scientific record. To prevent unstable signals from being fossilized into public health policies and clinical guidelines, we advocate for the mandatory adoption of dynamic bio-informatic audits and rigorous benchmarking as a standard prerequisite for large-scale observational research.
Marco Roccetti (Sat,) studied this question.