You wrote the paper to interpret your data, draw the clinical conclusions, and make the case that the work matters — that is what reviewers should judge, not statistical errors. RigorMD gives your draft an independent, outside read that catches the distractions first: it recomputes your reported numbers, checks each claim against its evidence and the published literature, and surfaces errors, weaknesses, and opportunities to place your work correctly.
An independent, outside read — statistical and methodological. The review before the reviewers.
Or verify your statistics free, in your browser — no upload, no account →
Not sure it fits your paper? See what we'll recompute for your study →
| 01 | Design / claim fit | Serious |
| 02 | Results / conclusion | Moderate |
| 03 | Statistical method | Serious |
| 04 | Reporting guideline | Mild |
| 05 | Numerical consistency | Critical |
▸ opened — the same trace stands behind every row In your Table 2In-hospital mortality — 4.2% vs 6.1% In your abstract“…mortality fell to 2.4%…” What to doYou revised the table but the abstract still carries the old figure. Reconcile them before a reviewer does. | ||
| 06 | Clinical verdict | Moderate |
| 07 | Contribution & positioning | Mild |
Built by a practicing academic surgeon and surgical journal editor with 61 peer-reviewed publications and 100+ editorial manuscript reviews.
See a sample report →The work is the author's, but it goes out stamped with the program's name — and it represents everyone who shares it. RigorMD helps the author and protects that reputation: one consistent methodological and statistical checkpoint for trainees, faculty, and clinical research groups before journal submission.
Authors keep their reports private. Leaders see adoption, workflow status, and aggregate usage — not findings or severity. The author gets a candid review they control; the program gets a common quality floor, an audit trail for process improvement, and fewer avoidable rejection cycles.
The cheapest thing to do is paste your manuscript into a general-purpose AI and ask if it's any good. Here's what that misses — and a reviewer won't. RigorMD is built differently: an adjudication system for clinical research — two independent engines appraise your paper blind, their findings are reconciled, serious ones are adversarially verified, and a deterministic layer neither engine controls recomputes your numbers.
A severity-scored report with deterministic statistical checks — grounded in your own quotes and numbers.
After you revise, the full review runs again — compared finding-by-finding with your first report: no longer flagged, still flagged, or new.
The free calculators run the same deterministic engines the paid review uses — no upload, no account. The $30 pre-submission review is the full instrument: an independent read of the whole manuscript — 16 deterministic checks plus a clinician + statistician appraisal.
Outcome type, groups, pairing, adjustment — the estimand-first recommendation, with its assumptions and its fallback.
Open the picker →How many covariates can your model actually support? Enter your events and candidate predictors against the events-per-variable rule.
Open the calculator →Do your DOIs and PMIDs actually resolve — and to the work you cited?
Check your references →The publication loop is slow, statistical review is scarce, and small methodological errors are common — and costly. RigorMD finds them before a reviewer does, so revision cycles get shorter and the decision letter holds fewer surprises.
Submission to publication runs from roughly 70 to 558 days across biomedical journals — and every revision or rejection round adds more. Catch the fatal flaw before a reviewer does.
Biostatistics expertise can be scarce, and academic statistical-support units report capacity constraints. Get a structured read when access is limited.
In one audit of orthopaedic journals, 17% of papers had a statistical error that could change the conclusion. Our deterministic layer recomputes the numbers it can and reconciles them against the text.
Biomedical retractions have quadrupled in 20 years. The statistical and interpretive errors we flag are a preventable share — and one weak paper can shadow an entire group.
And because this is clinical research, the deepest reason is the simplest: a flawed statistic becomes flawed care. Cleaner evidence is better medicine.
Manuscript, tables, figures, supplement, cover letter, title page. PDF and DOCX. Confirm what's included.
We classify the study design and claim type, appraise the manuscript across six domains, and a separate literature layer positions it against retrieved prior work as a seventh.
Deterministic checks recompute p-values, denominators, percentages, and means where the reported numbers allow. Every finding is traced to a quote.
A reconciled, severity-scored report — emailed when ready, stored in your private dashboard.
Full validation is not instant. Most reports return by email after processing; larger packages take longer.
Whether a causal, comparative, or equivalence claim is supported by the study design actually used.
Whether the abstract, results, and conclusions agree — without spin or reframed null findings.
Models, power, missing-data handling, and multiplicity, matched to the design and endpoints.
Design-specific reporting requirements, item by item, with the gaps named.
Whether numbers, denominators, p-values, intervals, tables, and text agree with one another.
What a clinician or editor can legitimately take from the manuscript as written.
Whether the manuscript's novelty and framing hold up against retrieved prior work — overstated firsts and unacknowledged priors flagged.
Every serious or critical finding is grounded in a direct quote or a recomputed number — so you can verify it, not just trust it.
“Surgical-site infection occurred in 18 of 90 patients (20.0%) in the bundle group versus 27 of 90 (30.0%) with standard care (χ² = 4.83; p = 0.028).”a constructed specimen — and this sentence is the check’s entire input
| study arm | SSI | no SSI | total |
|---|---|---|---|
| bundle | 18 | 72 | 90 |
| standard care | 27 | 63 | 90 |
| total | 45 | 135 | 180 |
≈ 58% of the headline gain does not survive the authors' own reverse-causation check.
Read a full sample report →See every check we run →Try a free check yourself →
RigorMD serves authors preparing a manuscript, journals and publishers triaging submissions, and departments standardizing pre-submission QC across research groups.
Pre-submission review, reviewer-response scaffolds, and re-review for medical manuscript authors who need a methods-and-statistics second read.
Structured editorial decision support to route manuscripts, focus reviewer attention, and identify methods or statistical follow-up needs.
One consistent review across trainees, faculty, and clinical research groups, with author-private reports and aggregate adoption visibility.
A reviewer's first property is independence. RigorMD had no hand in your paper — it exists only to find what a reviewer will. Each manuscript is appraised independently by two engines and reconciled into a consensus — disagreement is surfaced, not hidden. A deterministic layer recomputes statistics where the reported numbers allow, so a flag is a calculation you can check. Findings quote the manuscript directly and state, for every serious or critical issue, the direction of bias and its clinical consequence.
Tested against the public record: the engine independently found the documented reason PREDIMED was retracted →
One review, two readers. Every finding is written twice — a plain clinician line (what the study can and cannot support) over an inline “Why this matters statistically” bridge — the bias mechanism and the remedy — with the RoB 2 / ROBINS-I domain and GRADE rationale in a demoted technical panel. And the paper's headline conclusion gets a plain calibration grade: the gap between how confidently the authors state it and how much certainty the evidence can carry. A careful study stated humbly reads well; overreach is the flag, not observational data.
Fully automated, and we show our work: no human reads your manuscript as part of the analysis (operational access is limited to logged security review). Two independent engines and recomputed statistics produce a report you can verify yourself; delivery timing varies by package complexity and service availability.
And we are explicit about the limits: the report distinguishes checked and passed from not checkable from the submitted files. Without raw data, some questions can't be answered — and we say so. The report flags for your judgment and does not promise journal acceptance.
Built by a practicing academic surgeon and surgical journal editor with 61 peer-reviewed publications and 100+ editorial manuscript reviews. It exists for a simple reason: too much of the published literature does not hold up under methodological scrutiny, and the expertise that would catch the problems before submission is scarce, gated, and unevenly distributed.
This goes deeper than a general-purpose model can. The system is purpose-built on the research-on-research literature, on statistical standards, and on the reporting guidelines journals actually enforce — and its reports are written in the register physicians read. Catch the avoidable flaws before a reviewer does, and spend the months a rejection cycle costs on what your study actually shows.
Launch pricing — current rates apply to any report you start now. Every report is fully automated — two independent engines plus deterministic checks, with no human review in the analysis. Delivery time varies with package complexity and service availability. Pricing details & author FAQ →
What is RigorMD?
RigorMD is an automated pre-submission review for clinical research manuscripts. You upload a manuscript package — text, tables, figures, supplement — and receive a severity-scored methodological and statistical report before you submit to a journal: two independent engines appraise the paper across multiple passes, a deterministic layer recomputes your reported statistics, cited DOIs and PMIDs are resolved against Crossref and PubMed, and every finding is traced to your own text.
How is RigorMD different from pasting my manuscript into a chatbot?
A general-purpose chatbot reads your paper once and tends to agree with it — and the assistant that helped write or analyze a paper is grading its own homework. RigorMD is an independent adjudication system with no hand in your manuscript: two engines appraise the paper blind across multiple passes and only findings that recur are reported, recomputes your statistics from the reported numbers instead of trusting them, resolves every cited DOI and PMID against Crossref and PubMed, and traces each finding to your own text.
Does RigorMD actually check my statistics?
Yes. A deterministic layer recomputes p-values, denominators, percentages, and related values from the manuscript's reported numbers and flags any that do not reconcile — the recomputed number is shown in the finding.
Will it invent references the way a chatbot can?
No. Cited DOIs and PMIDs are resolved against Crossref, doi.org, and NCBI PubMed; identifiers that do not resolve, or that resolve to a different article than the one cited, are flagged in the report.
Is the report written for a clinician or a statistician?
Both. Every finding is written twice — a plain-language clinician line over a statistician panel that names the bias mechanism and its RoB 2 / GRADE domain — so the physician and the statistician can each act on it.
Does RigorMD read my paper in the context of the medical literature?
Yes. A dedicated literature layer retrieves related prior work from PubMed and weighs your framing and novelty claims against what it actually found — overstated firsts and unacknowledged prior studies are flagged, with the retrieved records cited in the report. Your manuscript is read in context, not in isolation.
Upload your package and we'll email you when the severity-scored report is ready. Delivery time varies with package complexity and service availability. Submit knowing what the toughest reviewer will see, because you've already seen it.