Published in a Q1 Surgical Journal With Zero Statistics Revisions
A surgical research manuscript for which GetBayes handled the statistical analysis was published in a Q1 (SCIE, first-quartile)-indexed international surgical journal — and throughout peer review, not a single revision request concerned the statistical methodology. Every revision comment addressed other sections, such as the introduction and discussion. The statistics section was accepted exactly as submitted.
Reviewers asking for additional analyses, effect sizes, or a different test for statistical reasons is extremely common and completely normal — close to standard practice at Q1 journals — and getting such a request never means the work was done poorly; if anything, it's a sign that peer review is doing its job. We're not sharing the absence of such a request here as a claim of perfection, but as one example of what's possible when the process is built correctly from the start.
This page walks through how the process worked and the concrete factors behind this outcome.
Who is this case a reference point for?
Researchers preparing a manuscript for a Q1/SCIE-indexed surgical or medical journal
Anyone evaluating statistics consulting who wants concrete proof of quality, not just claims
Authors who previously received a statistics-driven reviewer revision and want to avoid repeating it
Anyone who wants to verify GetBayes's working standard from an outside reference point
What happened
A surgical research team brought their study's statistical analysis to GetBayes before submission. Tests and models were chosen to match the research question and data structure, assumption checks were documented, and results were reported with effect sizes and confidence intervals in the target journal's format; the statistics paragraph of the manuscript's methods section was written from that same report.
The manuscript was submitted to a Q1 (SCIE)-indexed international surgical journal and went through peer review.
Outcome: zero statistics-related revision requests
When the reviewer reports came back, every revision comment addressed sections like the introduction, discussion and references. Not one question or correction concerned the statistical method, the tables, or how the results were reported — the statistics section was accepted exactly as first submitted.
Given how rigorous reviewers at Q1 journals typically are about statistics — most manuscripts get at least one statistics-driven revision round — we don't think this was chance. We see it as evidence of an analysis process that was built correctly from the start.
To be clear: getting a statistics-driven revision round is also completely normal and a sign that scientific peer review is working as intended — we routinely handle exactly these kinds of revision requests for a large share of the manuscripts we support. This case is the exception, not the rule or the expectation.
What shows this wasn't luck
By the time the report was delivered, the following were already in place:
Test and model choices matched to the study design (case series, cohort, comparative surgical study, etc.), justified in writing
Effect sizes and 95% confidence intervals were in the tables from the start, for every test
Assumption checks (normality, homogeneity of variance, etc.) were performed and documented in the methods section
Where confounders needed control, multivariable analysis was planned and reported from the start
The sample size / power justification was prepared before submission
Tables were built to the target journal's author guidelines and format from the start
Common reviewer requests vs. this case
The list below covers the statistics requests reviewers most often make on journal manuscripts, and why none of them came up in this case:
| Common reviewer request | Status in this case |
|---|---|
| Add effect sizes and confidence intervals | Already in the tables at first submission |
| Correct for multiple comparisons | The appropriate correction was applied and reported from the start |
| Demonstrate normality / use a nonparametric test | Assumption tests were documented in the methods section |
| Control for confounding variables | The needed multivariable analysis was planned from the start |
| Add a sample size / power justification | Prepared before submission |
| Match tables to the journal's format | Tables were built to the target journal's template |
Verification
This case is real and belongs to a study GetBayes completed. Once the authors consent, we'll add the journal name and a link to the article to this section; until then, this page describes the process and outcome without naming the authors or their institution.
Why this matters
Q1 means a journal sits in the top quartile (top 25%) of its field by citation impact, in classifications like SCIE/Scopus — and those journals' reviewer pools tend to be the most rigorous on statistics. Not getting a statistics-driven revision round means both a faster path to acceptance and an independent confirmation that the method was built correctly from the start — proof of consulting quality delivered through an outcome, not a claim.
To be clear: having received a statistics-driven revision round never means the work was done badly — it's a natural, expected part of scientific peer review. We routinely manage exactly this kind of revision for a large share of the manuscripts we support; this page shares one case with the best possible outcome, not a judgment on revisions themselves.
Frequently asked questions
What does Q1 mean?
Q1 means a journal ranks in the top quartile (25%) among journals in its field by citation impact factor, in classifications such as SCIE/Scopus. Q1 journals generally hold the highest peer-review standards.
Is this a real case?
Yes — a real study GetBayes completed, not a representative composite. Once the authors consent, we'll add the journal name and a link to the article; until then we describe it without naming the authors or institution.
Does every manuscript get this result?
No, we can't guarantee that — and no one honestly can: the outcome also depends on study design, data quality, and a given journal's or reviewer's specific expectations. Putting the right test choices, effect sizes/confidence intervals, assumption checks and journal-formatted tables in place from the start meaningfully lowers the risk — but getting a statistics-driven revision round is completely normal and never means the work was done badly; it's a natural part of scientific peer review, and we routinely handle exactly these requests.
Does this only apply to surgical journals?
No. We apply the same approach to any manuscript with quantitative data — medicine, dentistry, nursing, psychology and the social sciences included. See our journal article statistics page for more on our manuscript and reviewer-revision support.
My manuscript already has a reviewer revision — can you help?
Yes. Even if the original analysis was done elsewhere, we run the additional analyses reviewers ask for on the same dataset and prepare the statistical justification text for your response letter.
Which study designs do you work with for results like this?
Cross-sectional and descriptive studies, case-control, cohort, randomized controlled trials and case series — any quantitative surgical or clinical research design.
Let's build your manuscript's statistics with the same rigor
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Last updated: July 10, 2026