Rigor Reviewed

Rigor-Reviewed Manuscript

AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial.

Name: Αlpha¹ Rigor Record — AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial.
Item: AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial.
Rating: 86
Author: Αlpha¹

Elías-Cabot E, Romero-Martín S, Raya-Povedano JL, Rodríguez-Ruiz A, Álvarez-Benito M

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Αlpha¹ Rigor Review — 1.5★ (Critical), 0 critical, 5 high issues for “AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial.”

Links back to this Rigor Record.

Αlpha¹ Editorial

This rigor review was examined and confirmed by Αlpha¹ Editorial · August 10, 2026

Αlpha¹ Rigor Review

Rigor Reviewed

1.5 / 5

Critical — serious problems across many checks

Interventional Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

IntegrityIntegrity concern ×6−3★
ReportingStudy design partially met−0.25★
CitationsUnresolved reference−0.25★

Model-reported confidence: moderateReviewer agreement: lowEngine: 7.29.0Source: full text

Not verified in this report

The numeric-impossibility checks (GRIM/GRIMMER/DEBIT/SPRITE) did not run on this paper: the pass that reads its reported means did not complete. No reported mean was checked for arithmetic impossibility.

Problems surfaced8 types

Numerical inconsistencies2

Values that contradict each other or are impossible for the stated sample: recomputed p-values and test statistics, GRIM/GRIMMER checks on summary numbers, percentages against their own counts, totals against their parts, and estimates against their own confidence intervals.

Internal contradictions in the reported numbersAssessed
Mathematically impossible statisticAssessed

Overstated conclusions1

Conclusions that reach past what the paper's own results support — including a significance claim that no longer holds when the statistic is recomputed, and efficacy resting on an unvalidated surrogate endpoint.

Conclusions only partially backed by the presented evidenceAssessed

Data authenticity concerns2

An adversarial read for patterns associated with data that may not be genuine: results that look too clean, implausibly large effects, duplicated data or images, and methods that do not match the results reported.

Methods and results do not matchAssessed
Other integrity concernAssessed

Reporting gaps1

Required detail the manuscript never states — study design, biological variables, ethics approval and consent, key resources, statistical reporting, data and code availability, and overall transparency.

Study-design details incomplete (controls, blinding, power)Assessed

Broken references and links1

References checked against Crossref, OpenAlex and Retraction Watch for retractions and resolvability, plus declared data and code links probed for whether they resolve to content matching the paper.

References not resolvable to a published paperRecomputed

Copyediting1

Wording, consistency and formatting errors that need correcting before submission.

Wording or formatting errors that need correctingAssessed

This Αlpha¹ Rigor Review uses AI Rigor Reviewers trained on a curated corpus of high-fidelity and retracted papers, with expert supervision and curation. It can still make mistakes; verify each finding against the source before relying on it.

The paper reports a well-conducted prospective clinical trial with a strong scientific premise and adequate ethical approvals. However, several reporting gaps reduce its robustness: reader blinding is not addressed, missing data handling is unspecified, race/ethnicity and weight/health status are unreported, statistical analysis code is not shared, and the trial registration number is inconsistent and unresolvable. A potential discrepancy in the PPV p-value (0.500 vs ~0.97 from recomputation) requires investigation.

This assessment synthesizes three independent reviewer runs (all from the same model, AI) and a copyedit pass. The reviewers agreed on most dimensions, but there were disagreements on study design, biological variables, ethical approvals, statistical analysis, and data code availability. The synthesized statuses reflect the checklist-based scoring rules. The statistics verification component covered only 3 tests; the PPV p-value discrepancy was not verified by the component and is flagged by a reviewer's recomputation.

12 major claims checked against the paper's own evidence: all adequately supported.

Recomputed 3 tests: 3 consistent, 0 inconsistent; 3 via agent-written checks.

6 integrity concerns flagged (1 high).

Reporting

Eight Dimensions7 pass · 1 warn

The introduction cites numerous prior studies (refs. 1–22) establishing the background, including retrospective simulations and prospective trials. It acknowledges limitations of prior work (e.g., retrospective nature, need for prospective confirmation) and states how the current study addresses them (prospective, paired design, includes DBT). The hypothesis follows logically from the cited evidence.

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 95% conf

The paper establishes a strong scientific premise by citing extensive prior research on AI in breast cancer screening, including retrospective and prospective studies, and clearly links the rationale to the study objectives.

Evidence

direct quote[Introduction, paragraphs 1–4]

“Over the last few years, several retrospective studies have shown that radiologists improved their cancer detection accuracy when using an AI system as concurrent reading support”