What Calibration Means in Practice
Calibration is the accuracy of confidence, not the volume of opinion.
In an AI-oncology program, calibration is the ability to look at a model's prediction, an analyst's deck, or an enthusiastic founder slide and tell — with reasons — what is true, what is conditionally true, and what will not survive a pre-IND meeting.
Calibration also asks whether the outcome being optimized is the outcome that matters. Meeting a primary endpoint, obtaining approval, achieving adoption in practice, and delivering meaningful patient benefit are different claims. OncAdios states which claim the evidence supports and refuses to promote one into another.
Every claim of consequence in an OncAdios deliverable is retrieved, source-tiered, and externally cited. Every recommendation surfaces the reasoning trail, the strongest counterevidence, and the conditions under which the recommendation would change. This is not a process — it is the calibration discipline that makes the recommendation auditable.
Calibration also means knowing when the right path is not the path the published guidance describes. Fitting inside the guidance is often correct. Proposing beyond it, in dialogue with the agency, is sometimes correct. Telling the two apart is the work — the Sprint is built around exactly that question.
When the evidence does not support a recommendation, OncAdios refuses to make one. Refusal is part of the output, not the absence of one.
OncAdios is itself an AI-augmented practice. AI is the leverage layer — used in retrieval, drafting, source-tiering, adversarial review, and pattern recognition across decades of regulatory precedent — under senior clinical-regulatory judgment and the operating standard linked below. The point is not that AI is fast. It is that the calibration system above is how AI gets used safely when the stakes are this high. The operating standard →