Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Name: Αlpha¹ Rigor Record — Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.
Item: Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.
Rating: 96
Author: Αlpha¹

Adedinsewo DA, Morales-Lara AC, Afolabi BB, Kushimo OA, Mbakwem AC, Ibiyemi KF, Ogunmodede JA, Raji HO, Ringim SH, Habib AA, Hamza SM, Ogah OS, Obajimi G, Saanu OO, Jagun OE, Inofomoh FO, Adeolu T, Karaye KM, Gaya SA, Alfa I, Yohanna C, Venkatachalam KL, Dugan J, Yao X, Sledge HJ, Johnson PW, Wieczorek MA, Attia ZI, Phillips SD, Yamani MH, Tobah YB, Rose CH, Sharpe EE, Lopez-Jimenez F, Friedman PA, Noseworthy PA, Carter RE, SPEC-AI Nigeria Investigators

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Αlpha¹ Rigor Review — 2.75★ (Weak), 0 critical, 3 high issues for “Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.”

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Αlpha¹ Editorial

This rigor review was examined and confirmed by Αlpha¹ Editorial · August 16, 2026

Αlpha¹ Rigor Review

Rigor Reviewed

2.75 / 5

Weak — serious problems found

Interventional Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

IntegrityIntegrity concern ×2−1★
ClaimsEfficacy rests on an unvalidated surrogate endpoint−0.5★
ClaimsTreatment effect not shown to be clinically meaningful−0.5★
ReportingData & code availability partially met−0.25★

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

Problems surfaced5 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 conclusions3

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.

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

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.

Data/code availability incompleteAssessed

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.

This is a well-conducted and transparently reported pragmatic randomized trial with a strong scientific premise, rigorous design, and appropriate statistical analysis. The main weakness is the vague data availability statement and lack of code sharing, which limits reproducibility.

Both reviewers independently scored all eight dimensions and agreed on every status; no divergence to reconcile. The statistics verification recomputed 6 tests (all consistent) but does not cover all reported statistics; the citation check found no retracted or unresolved references; the reproducibility check found both links live.

7 major claims checked against the paper's own evidence: 2 only partially supported (evidence backs part of the claim; gaps or caveats remain); the rest adequately supported.

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

Recomputed 6 tests: 6 consistent, 0 inconsistent; 5 recomputed directly from the reported test statistics, 1 via agent-written checks.

2 integrity concerns flagged (0 high).

Reporting

Eight Dimensions7 pass · 1 warn

The introduction cites multiple prior studies (retrospective and pilot prospective) showing AI-ECG effectiveness, and notes the high incidence of peripartum cardiomyopathy in Nigeria. The rationale for the trial is clearly stated: to determine whether AI-guided screening improves detection beyond standard care. Limitations of prior work (e.g., small pilot samples, retrospective designs) are implicitly addressed by conducting a large prospective randomized trial.

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 prior research on AI-ECG for LVSD detection and the high burden of peripartum cardiomyopathy in Nigeria, and clearly links this to the study objective.

Evidence

direct quote[Introduction, paragraph 2]

“A retrospective study (area under the curve (AUC) = 0.89) and a pilot prospective study among pregnant and postpartum women in the United States showed AI-based screening to be effective (AUC = 1.00 using a 12-lead ECG and 0.98 using a digital stethoscope) in identifying pregnancy-related LVSD with LVEF < 45%.”