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: 100
Author: Αlpha¹

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

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Αlpha¹ Rigor Review — 1.25★ (Critical), 0 critical, 6 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 15, 2026

Αlpha¹ Rigor Review

Rigor Reviewed

1.25 / 5

Critical — serious problems across many checks

Interventional Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

IntegrityIntegrity concern ×4−2★
ClaimsUnsupported claim (uncorroborated)−0.5★
ClaimsEfficacy rests on an unvalidated surrogate endpoint−0.5★
ClaimsTreatment effect not shown to be clinically meaningful−0.5★
CitationsUnresolved reference−0.25★

Model-reported confidence: highReviewer agreement: highEngine: 7.39.0Source: full text

Problems surfaced7 types

Numerical inconsistencies1

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

Overstated conclusions4

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 not supported by the paper’s own evidenceAssessed
Efficacy rests on an unvalidated surrogate endpointAssessed
Treatment effect not shown to be clinically meaningfulAssessed
Conclusions only partially backed by the presented evidenceAssessed

Data authenticity concerns1

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.

Other integrity concernAssessed

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

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 is a well-designed prospective, paired, noninferiority clinical trial evaluating an AI-based triage strategy in breast cancer screening. It demonstrates strong scientific premise, rigorous design, adequate reporting of biological variables, ethics, key resources, statistics, data availability, and transparency. However, there are minor reporting gaps (blinding details, exact p-values, reporting guideline) and a critical issue with the trial registration number that could not be resolved.

Both reviewers classified the study as interventional, which is adopted. The evaluation covered all eight dimensions, with several sub-criteria marked as not applicable (e.g., species/strain, housing, antibodies). The statistics verification checked only 1 test (consistent), and the citation check found 1 reference not found in registry (the Zenodo DOI). The integrity check flagged the trial registration number as unresolvable.

9 major claims checked against the paper's own evidence: 1 not fully backed by the presented evidence (unsupported or overstated), 2 only partially supported (evidence backs part of the claim; gaps or caveats remain).

Premise concern: surrogate not validated for clinical benefit; effect size not shown to be clinically meaningful.

Recomputed 1 test: 1 consistent, 0 inconsistent; 1 via agent-written checks.

4 integrity concerns flagged (1 high).

Reporting

Eight Dimensions8 pass

The introduction cites multiple prior studies (retrospective and prospective) showing AI's potential to improve screening accuracy and reduce workload, and explicitly notes limitations of retrospective studies (e.g., 'simulated retrospective studies'). The premise is logically linked to the study objectives: to prospectively confirm that AI can safely omit human reading for low-risk exams. The paper also addresses how the current study overcomes prior limitations by being prospective and including DBT.

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedADEQUATE

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 90% conf

The paper establishes a strong scientific premise by citing extensive prior research and clearly linking it to the study objectives.

Evidence

direct quote[Introduction, paragraphs 1-3]

“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 – . Furthermore, AI systems implemented as a stand-alone solution have been found to achieve human-like performance in simulated retrospective studies – , classifying exams according to cancer probability and resulting in simulations of AI-based breast cancer strategies that suggested improved clinical outcomes ,– .”