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Fair Representation & Reverse Audit
CIDM iReview · manuscript quality · public learning layer

Practical manuscript checks, learned from reviewing our own corpus.

This page turns CIDM iReview into a practical pre-submission resource: clearer denominators, safer causal language, better validation claims and more consistent tables. The review is living and correction-aware. It is not a scorecard of authors, teams or papers.

Why this is useful

Use the review before the next manuscript leaves the lab.

The most useful output of a large literature review is not a list of faults. It is a reusable set of checks that helps future papers become clearer, easier to review and less likely to need correction later.

Before submissionRun the 10 checks below against the abstract, tables and key claims.
During peer reviewSeparate genuine manuscript problems from normal scientific uncertainty.
After publicationReturn corrections or context without erasing the original review trail.
For traineesUse anonymised patterns as teaching examples rather than naming individuals.
1,947unique CIDM publication objects in the current corpus
963substantive writing-issue screens used for current prevalence
984metadata/title triage records — not counted as “no issue”
98effective review signals after reverse-audit correction
Current review state

98 evidence-supported signals — not an error count

5Specific reconciliation queries
Concrete arithmetic, table or label inconsistencies worth resolving. These remain queries until source or author return clarifies them.
86Paper-specific review queries
Points worth checking or clarifying; not confirmed errors and not a ranking of papers.
7Writing improvements
Interpretation or wording could be tightened; explicitly not an error label.
0 LOWLow-certainty author-facing signals
Reverse audit removed LOW-certainty items from the public-facing signal set.

Current effective signal prevalence among substantively screened papers: 98 / 963 = 10.2%. This is a review-signal rate, not an error rate.

Fairness gates

The review must be reviewable too.

Reviewer caution ≠ manuscript errorStudy limitation ≠ writing errorNot visible to us ≠ missing from the paperNeeds validation ≠ invalidCitation ≠ evidence

1 · ScreenRecord a candidate issue without calling it an author error.
2 · ChallengeAsk whether the evidence is paper-specific or just generic caution.
3 · CorrectRemove reviewer false positives and preserve the correction history.
4 · Human returnAuthor/source feedback can confirm, remove or reclassify a signal.
CIDM Manuscript Quality · 10 checks

Ten practical checks before submission

1

Reconcile every denominator

Totals, subgroups, percentages and analysed N should agree.

2

Name the unit of analysis

Patients, samples, isolates, tests and episodes are not interchangeable.

3

Separate prediction from validation

Retrospective association or model fit is not prospective clinical utility.

4

Separate association from causation

Temporal sequence, observational association or ex-vivo function does not by itself establish a causal clinical effect.

5

“Not detected” is not “absent”

Negative detection does not prove zero prevalence or zero risk.

6

Genomic relatedness is not direct transmission

Sequence similarity can support linkage hypotheses but does not prove transmission direction on its own.

7

Predicted resistance is not confirmed resistance

Keep genotype-predicted and phenotype-confirmed resistance separate, with the confirmation denominator visible.

8

Comparator ≠ gold standard

Agreement with another assay is not independent validation against a reference standard.

9

Subset results need subset denominators

Keep tested, available and confirmed subsets attached to their exact numerator and denominator.

10

Tables, labels and identifiers must agree

Abstract, main text, tables, figures, supplements and specimen IDs should tell the same numerical story.

Living correction model

Corrections improve the review rather than weaken it.

ADDNew evidence supports a new issue or better category.
REMOVEA reviewer-side false positive leaves effective statistics without erasing history.
CONFIRMSource or human return supports the current interpretation.
RECLASSIFYThe issue is real, but the original category was wrong.

The raw screen is historical evidence about the review process. The correction ledger is part of the scientific record, not an embarrassment to hide.

Public-use boundary

Teaching before attribution

The public page deliberately keeps teaching examples anonymised. Named attribution should wait for stronger source return, author clarification, or a clear scientific reason that identification improves understanding. The default use is education, manuscript quality improvement and transparent correction — not fault assignment.

AI review remains preliminary scientific assistance. Human Gate is OPEN; Formal Accept = 0. Author confirmation is representation evidence, not a truth verdict.

Return a correction or expert judgement →

Current control boundary · 4 Oct 2026

Corpus: 1,947 exact publication objects. First-pass Deep iReview: 1,947 / 1,947 closed. Active depth: 258 FULL · 1,544 ABSTRACT · 145 METADATA. The writing-issue living audit currently uses 963 substantive screens and 984 metadata/title triage records; these are different layers and should not be conflated.

Public statistics on this page inherit the living audit and reverse-audit ledger. They should be updated when the underlying evidence changes; do not independently recompute them for a slide or manuscript.