ManAudit

Forensic manuscript error audit — for any AI.

Copy the prompt below, open Claude, Gemini, or ChatGPT, paste it, and attach a manuscript (PDF). The model runs a structured forensic review: it hunts internal numerical inconsistencies, denominators that don't reconcile, headline numbers that drift across the abstract, tables, and figures, duplicate or missing references, incoherent diagnostic metrics, missing predictive values at population prevalence, extreme heterogeneity feeding a single pooled estimate, definition drift, reverse causation in screening studies, and conclusions that outrun the data — then returns an errors table, major and minor issues, and an editorial recommendation.

How to use it

Copy the prompt

Use the Copy button below. Nothing to fill in — it's ready to send.

Open your AI

Claude, Gemini, or ChatGPT. Use a model that accepts file uploads.

Paste & attach

Paste the prompt, attach the manuscript PDF, and send.

The prompt

ManAudit — paste into any AI, then attach a manuscript

Open an AI

Claude →

claude.ai — strong on long documents and tables.

Gemini →

gemini.google.com — large context for full PDFs.

ChatGPT →

chatgpt.com — attach the PDF, then paste.

How it behaves. The prompt ends by telling the model to begin if a manuscript is attached, or to ask you for one if it isn't. The model reasons before answering. Verify before you rely on it: AI structures the review and surfaces candidate errors — every flagged number, citation, and claim should be confirmed against the primary source. This is a reviewer's aid, not a substitute for editorial judgment. What the checks are based on →

← Back to the tool

What the checks are based on

ManAudit doesn't introduce new clinical thresholds — it operationalizes established reporting and quality-appraisal standards and applies them as a consistency and coherence check. The audit logic draws on the following.

Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
Flow-diagram arithmetic and reporting completeness for systematic reviews.
Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology (MOOSE): a proposal for reporting. JAMA. 2000;283(15):2008-2012.
Reporting standard for meta-analyses of observational data.
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Lancet. 2007;370(9596):1453-1457.
Reporting standard for observational and registry analyses.
Reitsma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982-990.
Basis for flagging separately-pooled, internally incoherent diagnostic summary points; bivariate model as the standard.
Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med. 2001;20(19):2865-2884.
HSROC modeling for diagnostic-accuracy synthesis.
Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529-536.
Risk-of-bias appraisal for diagnostic studies.
Wells GA, Shea B, O'Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute.
Quality appraisal of cohort and case-control studies; basis for scrutinizing non-discriminating uniform scores.
Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32-35.
Youden index consistency check (sensitivity + specificity − 1).