What it does
RefCheckr is a closed-loop AI system for evidence-supported, ABPI-compliant claims. It verifies claims against the cited source paper, rewrites any that don’t match, then re-verifies the rewrite and checks it for ABPI compliance — looping again if anything fails. The output is a claim that has been proven against the paper, not just generated.The problem it solves
A 20-page detail aid with 40 references takes a senior medical writer 2–3 hours to reference-check manually. After three rounds of revision, accuracy drifts: a hazard ratio gets loosely paraphrased as a percentage reduction, a p-value changes in the text but not in the table, a claim is reworded and now extends beyond what the source actually says. These are among the most common MLR rejection reasons and the most time-consuming to fix late in the approval cycle. RefCheckr doesn’t just flag problems — it works the claim until it is supported by the source and aligned with the ABPI Code, then hands a verified claim back for human review.How the closed loop works
Where it fits in the playbook
RefCheckr is most relevant in these workflows:How to use it in a workflow
- Prepare your content — Provide the claim and the source paper (or DOI, PMID, or uploaded file)
- Run RefCheckr — The loop runs automatically: verify, fix, re-verify, ABPI compliance check
- Review the output — RefCheckr returns the rewritten, verified, compliance-checked claim with the supporting evidence cited
- Decide what to keep — A human reviewer accepts, edits, or rejects the rewrite before it enters MLR
What it does well
- Verifies claims against the source paper, detecting mismatches in magnitude, endpoint, or population
- Rewrites claims using only evidence from the paper, with guardrails against new numbers, new endpoints, or stronger language
- Re-verifies the rewrite and checks it for ABPI compliance before returning it
- Returns claims that are both evidence-supported and code-aligned, with citations back to the source
What it does not do
- Does not provide final regulatory or compliance clearance. RefCheckr is a verification and rewrite tool, not a regulatory approval system. MLR review is still required.
- Does not assess whether the right references were chosen. It checks claims against the references provided, not whether better or more appropriate references exist.
- Does not replace a trained medical writer’s review. A human must still assess context, appropriateness, and editorial judgement on the rewritten claim.
Risk tier
RefCheckr is used in high-risk workflows. A clean closed-loop output does not mean the document is approved — it means the AI has produced a claim that passed its own verification and compliance checks. Manual review by a qualified medical writer or reviewer remains essential.Complementary tools
RefCheckr focuses on a specific task: producing a claim that is verified against the source and aligned with the ABPI Code. Other tools serve different parts of the reference workflow:- Scite.ai — citation context analysis. Shows whether a citation supports, contrasts, or merely mentions a claim. Useful as a second layer alongside RefCheckr’s claim verification.
- Zotero / EndNote — store, organise, and cite references. Use these to manage your reference library; use RefCheckr to verify claims against those references.
- Elicit — extract and compare findings across papers. Useful when building the evidence base before writing, while RefCheckr is used after writing to verify and rewrite for accuracy.