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Kaimen Rigor review

A large language model for complex cardiology care.

O'Sullivan JW, Palepu A, Saab K, Weng WH, Amponsah DK, Cheng E, Cheng Y, Chu E, Desai Y, Elezaby A, Fazal M, Hussain T, Jain SS, Kim DS, Lan R, Li J, Tang W, Tapaskar N, Parikh V, Sandoval R, Spencer-Bonilla G, Wu B, Kulkarni K, Mansfield P, Webster D, Gottweis J, Barral J, Schaekermann M, Tanno R, Mahdavi SS, Natarajan V, Karthikesalingam A, Ashley E, Tu T

DOI: 10.1038/s41591-025-04190-9
Record issued: 2026-08-15
Engine: 7.39.0
Exported: 2026-09-20

Prepared by Alpha1. This document is confidential: it is intended for the recipient it was shared with and must not be redistributed. The live record at alpha1science.com/verify/8ab6e220-12e8-4828-b456-b9f6a160a566 is authoritative.

Kaimen Rigor review

Kaimen Rigor reviewed

1.75 / 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★
ClaimsEfficacy rests on an unvalidated surrogate endpoint−0.5★
ClaimsTreatment effect not shown to be clinically meaningful−0.5★
ReportingEthical approvals partially met−0.25★

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

Biggest issues

01Efficacy rests on an unvalidated surrogate endpoint
highOverreachreviewer-assessedFull detail
The primary efficacy claim is that AMIE-assisted cardiologists produce 'preferable' assessments, with fewer errors and missing content, as rated by subspecialists. This is a surrogate outcome (subspecialist preference and error counts) rather than a hard clinical outcome. The paper does not demonstrate target engagement (e.g., PK/PD) nor cite validated evidence linking these surrogate measures to improved patient outcomes. The authors acknowledge this limitation: 'preference-based evaluation cannot definitively establish real-world clinical benefit.'
“preference-based evaluation cannot definitively establish real-world clinical benefit”
Find in source
02Treatment effect not shown to be clinically meaningful
highOverreachreviewer-assessedFull detail
The reported effect sizes are modest and not anchored to a minimal clinically important difference or clinical meaningfulness. For example, overall preference was 46.7% vs 32.7% (a 14% absolute difference), and error rates were 13.1% vs 24.3% (an 11.2% difference). These are statistically significant but the clinical significance is not established. The paper does not provide a benchmark for what constitutes a clinically meaningful improvement in these surrogate measures.
“subspecialists favored large language model-assisted responses overall, and for the management plan and diagnostic testing domains, with the remaining domains considered a tie”
Find in source
03Other integrity concern
mediumAuthenticityreviewer-assessedFull detail
Trial NCT06935253 was first submitted to ClinicalTrials.gov on 2025-04-11, after the registered study start date of 2025-01-10. Retrospective registration means the protocol and outcomes were not on the public record before the study ran, which is what prospective registration exists to establish.
NCT06935253
reviewer’s wording

Findings by category

101ArithmeticNumerical inconsistencies 302OverreachOverstated conclusions 103AuthenticityData authenticity concerns 104DisclosureReporting gaps 05AvailabilityNone found 06Copyedit6 minor suggestions

This Kaimen Rigor review uses Kaimen 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.

A well-conducted and well-reported RCT of LLM assistance for general cardiologists, with minor reporting gaps (sex not reported, no named IRB, no power analysis, code repository not linked).

Both reviewers classified the study as interventional (RCT). The design is a randomized controlled trial with blinded evaluation. The main divergence between reviewers was on biological variables (Reviewer 1 pass, Reviewer 2 warn due to missing sex) and on key resources (Reviewer 1 pass, Reviewer 2 pass with a note on the Python version typo). The synthesized statuses reflect the more conservative reading where a reporting gap exists.

Numerical inconsistencies

1 finding · worst low

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

Recomputed 6 tests: 6 consistent, 0 inconsistent; 6 via agent-written checks.

Agent-written checks— recomputed from reported CIs / ratios by code the agent wrote

CONSISTENTreported p = .020 · recomputed p = .036Reviewers 1, 2
Overall preference: 46.7% vs 32.7% with P=0.02
“subspecialists preferring 46.7% of all cases compared to 32.7% for unassisted cardiologists ( P = 0.02).”
Find in source
Taken as given: The percentages are out of 107 cases each.; The counts are rounded to integers: 46.7% of 107 ≈ 50, 32.7% of 107 ≈ 35.; The test is a two-proportion z-test, approximated by chi-square.
Method: Recomputed using Pearson chi-square on 2x2 table from rounded counts.
How we recomputed it: pChi2x2(50,57,35,72)
CONSISTENTreported p = .008 · recomputed p = .017Reviewers 1, 2
Management preference: 45.8% vs 29.9% with P=0.008
“AMIE-assisted assessments were preferred for 45.8% of the cases compared to 29.9% for unassisted cardiologists ( P = 0.008).”
Find in source
Taken as given: The percentages are out of 107 cases each.; The counts are rounded to integers: 45.8% of 107 ≈ 49, 29.9% of 107 ≈ 32.; The test is a two-proportion z-test, approximated by chi-square.
Method: Recomputed using Pearson chi-square on 2x2 table from rounded counts.
How we recomputed it: pChi2x2(49,58,32,75)
CONSISTENTreported p = .030 · recomputed p = .048Reviewers 1, 2
Diagnosis preference: 43.9% vs 30.8% with P=0.03
“AMIE-assisted responses were preferred for 43.9% of the cases compared with 30.8% for the unassisted ( P = 0.03).”
Find in source
Taken as given: The percentages are out of 107 cases each.; The counts are rounded to integers: 43.9% of 107 ≈ 47, 30.8% of 107 ≈ 33.; The test is a two-proportion z-test, approximated by chi-square.
Method: Recomputed using Pearson chi-square on 2x2 table from rounded counts.
How we recomputed it: pChi2x2(47,60,33,74)
CONSISTENTreported p = .033 · recomputed p = .035Reviewer 1
Error rate: 13.1% vs 24.3% with P=0.033
“13.1% of AMIE-assisted responses contained errors compared with 24.3% for unassisted responses (11.2% difference, P = 0.033).”
Find in source
Taken as given: The percentages are out of 107 responses each.; The counts are rounded to integers: 13.1% of 107 ≈ 14, 24.3% of 107 ≈ 26.; The test is McNemar's test on paired data, approximated by chi-square on discordant pairs.
Method: Recomputed using McNemar's test approximated by chi-square on discordant pairs (assuming discordant counts are 14 and 26).
How we recomputed it: pChi2x2(14,93,26,81)
CONSISTENTreported p = .002 · recomputed p = .001Reviewers 1, 2
Missing content: 17.8% vs 37.4% with P=0.0021
“there was significantly less missing content for AMIE-assisted responses: 17.8%, compared with 37.4% for unassisted responses (19.6% difference, P = 0.0021).”
Find in source
Taken as given: The percentages are out of 107 responses each.; The counts are rounded to integers: 17.8% of 107 ≈ 19, 37.4% of 107 ≈ 40.; The test is McNemar's test on paired data, approximated by chi-square on discordant pairs.
Method: Recomputed using McNemar's test approximated by chi-square on discordant pairs (assuming discordant counts are 19 and 40).
How we recomputed it: pChi2x2(19,88,40,67)
CONSISTENTreported p = .033 · recomputed p = .035Reviewer 2
Clinically significant errors: AMIE-assisted vs unassisted (13.1% vs 24.3%)
“13.1% of AMIE-assisted responses contained errors compared with 24.3% for unassisted responses (11.2% difference, P = 0.033).”
Find in source
Taken as given: The percentages are out of 107 cases.; The counts are rounded to integers: 13.1% of 107 ≈ 14, 24.3% of 107 ≈ 26.; The test is McNemar's test on paired data, approximated by chi-square on discordant pairs.
Method: McNemar's test approximated by chi-square on discordant pairs (assuming paired data).
How we recomputed it: pChi2x2(14,93,26,81)

lowinternal contradiction
The percentage of cases with no hallucinations (91.6%) and with clinically significant hallucinations (6.5%) do not sum to 100%, leaving 1.9% unaccounted.
“In 91.6% of cases, there were no reported hallucinations, while in 6.5% of cases, a likely clinically significant hallucination was observed.”
ResultsFind in source
lowinternal contradiction
Table 1 percentages for diagnoses sum to 98.2%, not 100%, likely due to rounding.
HCM 22 (20.6%) | Left ventricular noncompaction 21 (19.6%) | Dilated cardiomyopathy 8 (7.5%) | Arrhythmogenic cardiomyopathy 11 (10.3%) | Ischemic cardiomyopathy 11 (10.3%) | Other genetic 11 (10.3%) | Non-genetic/general 21 (19.6%)
Table 1reviewer’s wording

Overstated conclusions

3 findings · worst high

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

9 major claims checked against the paper's own evidence: 1 only partially supported (evidence backs part of the claim; gaps or caveats remain); the rest adequately supported.

partialReviewers 1, 2
LLMs could help bridge unmet needs in genetic cardiovascular disease and possibly in cardiac care more broadly.
The evidence supports potential, but the claim extends beyond the specific study setting.
Evidence: RCT results in a single center with retrospective data.
“Our evidence suggests that LLMs could help bridge unmet needs in genetic cardiovascular disease and possibly in cardiac care more broadly.”
Discussion ¶2Find in source
supportedReviewers 1, 2
Subspecialists favored LLM-assisted responses overall.
The presented preference data (46.7% vs 32.7%, P=0.02) directly supports this claim.
Evidence: Direct preference results in Figure 3a and text.
“subspecialists favored large language model-assisted responses overall”
AbstractFind in source
supportedReviewer 1
Cardiologists alone had more clinically significant errors and more missing content than cardiologists assisted by AMIE.
The error and missing content rates with p-values support this claim.
Evidence: Individual assessment results: errors 24.3% vs 13.1% (P=0.033), missing content 37.4% vs 17.8% (P=0.0021).
“Cardiologists alone had more clinically significant errors (24.3% versus 13.1%, P = 0.033) and more missing content (37.4% versus 17.8%, P = 0.0021) than cardiologists assisted by AMIE.”
AbstractFind in source
supportedReviewer 1
AMIE helped cardiologists more than half the time and saved time in 50.5% of cases.
Self-reported survey data from cardiologists support this claim.
Evidence: General cardiologist feedback: 57.0% reported help, 50.5% reported time savings.
“cardiologists who used AMIE reported that AMIE helped their assessment more than half the time (57.0%) and saved time in 50.5% of cases.”
AbstractFind in source
supportedReviewers 1, 2
The study demonstrates feasibility of using LLMs to assess patients with rare and life-threatening cardiac conditions.
The RCT results and qualitative feedback support feasibility, though the claim is modest.
Evidence: Overall preference and error reduction results.
“Our results demonstrate the feasibility of using LLMs to assess patients with rare and life-threatening cardiac conditions.”
Discussion ¶2Find in source
supportedReviewer 2
AMIE-assisted responses had fewer clinically significant errors.
The reported error rates and p-value support this claim.
Evidence: Errors: 13.1% vs 24.3%, P=0.033.
“Cardiologists alone had more clinically significant errors (24.3% versus 13.1%, P = 0.033)”
AbstractFind in source
supportedReviewer 2
AMIE-assisted responses had less missing content.
The reported missing content rates and p-value support this claim.
Evidence: Missing content: 17.8% vs 37.4%, P=0.0021.
“more missing content (37.4% versus 17.8%, P = 0.0021)”
AbstractFind in source
supportedReviewer 2
AMIE saved time in 50.5% of cases.
This is based on self-reported data from cardiologists, which is subjective but directly reported.
Evidence: Self-reported time savings in 50.5% of cases.
“saved time in 50.5% of cases”
AbstractFind in source
supportedReviewer 2
AMIE helped cardiologists in more than half of cases.
Self-reported helpfulness in 57.0% of cases supports this claim.
Evidence: Self-reported helpfulness in 57.0% of cases.
“AMIE helped their assessment more than half the time (57.0%)”
AbstractFind in source

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

INADEQUATESurrogate endpoint
The primary efficacy claim is that AMIE-assisted cardiologists produce 'preferable' assessments, with fewer errors and missing content, as rated by subspecialists. This is a surrogate outcome (subspecialist preference and error counts) rather than a hard clinical outcome. The paper does not demonstrate target engagement (e.g., PK/PD) nor cite validated evidence linking these surrogate measures to improved patient outcomes. The authors acknowledge this limitation: 'preference-based evaluation cannot definitively establish real-world clinical benefit.'
“preference-based evaluation cannot definitively establish real-world clinical benefit”
Find in source
INADEQUATEEffect size
The reported effect sizes are modest and not anchored to a minimal clinically important difference or clinical meaningfulness. For example, overall preference was 46.7% vs 32.7% (a 14% absolute difference), and error rates were 13.1% vs 24.3% (an 11.2% difference). These are statistically significant but the clinical significance is not established. The paper does not provide a benchmark for what constitutes a clinically meaningful improvement in these surrogate measures.
“subspecialists favored large language model-assisted responses overall, and for the management plan and diagnostic testing domains, with the remaining domains considered a tie”
Find in source

Data authenticity concerns

1 finding · worst medium

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

4 integrity concerns flagged (0 high).

mediumother
Trial NCT06935253 was first submitted to ClinicalTrials.gov on 2025-04-11, after the registered study start date of 2025-01-10. Retrospective registration means the protocol and outcomes were not on the public record before the study ran, which is what prospective registration exists to establish.
NCT06935253
reviewer’s wording
lowother
The paper states '93.4%' in one place and '93.5%' in another for the same metric (AMIE did not miss anything).
AMIE did not miss anything in 93.4% of cases. ... did not miss clinically significant findings in 93.5% of cases
Discussionreviewer’s wording

Reporting gaps

1 finding · worst medium

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.

Ethics/consent reporting incompleteAssessed

Eight Dimensions7 pass · 1 warn

The introduction cites WHO workforce shortages, HCM prevalence, and prior LLM studies, acknowledging the lack of RCTs and real-world data. The rationale for using genetic cardiomyopathies as an indicative example is logical. Limitations of prior work (e.g., simulated data, text-only) are explicitly addressed by using real-world multimodal data.

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 clear premise: scarcity of subspecialist expertise, and prior work on LLMs in medicine is cited, with gaps identified.

Evidence

“Despite more than 500 observational LLM papers published in 2024, systematic reviews of LLMs in medicine have consistently shown a lack of RCTs”

Discussion ¶5Find in source

“This study probes the potential of LLMs to democratize subspecialist-level expertise by focusing on an indicative example: the domain of genetic cardiomyopathies like HCM.”

Introduction ¶4Find in source

“Compared to earlier studies relying on simulated or text-only data, our design represents a substantial advancement toward real-world clinical applicability.”

Introduction ¶4Find in source

PASSReviewer 2· 90% conf

The paper establishes a clear premise of subspecialist scarcity and the potential of LLMs, citing prior work and limitations.

Evidence

“Despite the potential of LLMs to enhance medical expertise, rigorous assessment of their performance remains scarce in medical specialties, with few openly available datasets for model evaluation and almost no randomized controlled trials (RCTs) performed.”

Introduction ¶2Find in source

“This study probes the potential of LLMs to democratize subspecialist-level expertise by focusing on an indicative example: the domain of genetic cardiomyopathies like HCM.”

Introduction ¶3Find in source

“Compared to earlier studies relying on simulated or text-only data, our design represents a substantial advancement toward real-world clinical applicability.”

Introduction ¶3Find in source

Randomization method is not explicitly detailed (e.g., random number generator), but the unit (general cardiologists) and counterbalanced design are stated. Blinding of subspecialists is clearly described. Power analysis is not reported, but the study is a pilot with 107 cases. Inclusion/exclusion criteria are implicit (consecutive patients from SCICD). Outlier handling is not explicitly addressed, but the analysis uses paired comparisons. Controls are inherent (unassisted arm). Independent replication is not applicable for a single RCT.

Sub-criteria

controlsADEQUATE

power analysisNOT REPORTED

blinding levelsADEQUATE

outlier handlingNOT REPORTED

randomization unitADEQUATE

inclusion exclusionINADEQUATE

randomization methodINADEQUATE

replicate distinctionN/A

independent replicationN/A

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 85% conf

The RCT design is well-described: randomization of cardiologists, blinding of subspecialist evaluators, and a pre-specified analysis plan.

Evidence

“One of the two general cardiologists was randomized to complete the assessment with the assistance of AMIE.”

MethodsFind in source

“The subspecialists were blinded to the source of each assessment, and the assessments were provided in a randomized order.”

MethodsFind in source

absence[Methods, Statistical analysis]

No power analysis is reported.

PASSReviewer 2· 85% conf

The RCT design is rigorous with randomization, blinding of evaluators, and a pre-specified analysis, though some details like allocation concealment are not reported.

Evidence

“One of the two general cardiologists was randomized to complete the assessment with the assistance of AMIE.”

MethodsFind in source

“The subspecialists were blinded to the source of each assessment, and the assessments were provided in a randomized order.”

MethodsFind in source

absence[Methods, Statistical analysis]

No power analysis is reported.

The paper reports median age (59 years, range 18-96) and the distribution of cardiac conditions. Sex is not explicitly reported in the main text, but the demographics are adequate for a clinical study. Age and health status are covered by the disease categories. Species/strain and housing are not applicable.

Sub-criteria

demographicsINADEQUATE

sex reportedNOT REPORTED

sex justifiedN/A

age weight healthADEQUATE

housing conditionsN/A

species strain sourceN/A

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 80% conf

Patient demographics (age, sex) and clinical characteristics are reported, though sex distribution is not explicitly given.

Evidence

“The median age of the patients was 59 years (range 18–96 years).”

Results ¶1Find in source

absence[Results, paragraph 1]

Sex of patients is not reported.

paraphrase[Table 1]

PASSReviewer 2· 90% conf

Patient demographics and clinical characteristics are reported, including age, sex distribution, and disease categories.

Evidence

“The median age of the patients was 59 years (range 18–96 years).”

Results ¶1Find in source

paraphrase[Table 1]

absence[Results, paragraph 1]

Sex of patients is not reported.

The paper states that the study used retrospective de-identified data outside IRB oversight, and informed consent was obtained from physicians. However, no named IRB or ethics committee is mentioned, and the justification for exemption is brief. Regulatory compliance with the Declaration of Helsinki is stated. For human subjects research, both ethics approval and consent are needed; here consent for physicians is mentioned, but patient data exemption is not fully documented.

Sub-criteria

iacuc statementN/A

informed consentINADEQUATE

irb ethics statementINADEQUATE

regulatory complianceADEQUATE

Per reviewer (2) — the votes behind the verdict

WARNReviewer 1· 90% conf

The study involves retrospective de-identified patient data and physician participants; ethics approval is claimed to be outside IRB oversight, but no named IRB or explicit waiver is provided.

Evidence

“This study used only retrospective, de-identified data that fell outside the scope of institutional review board oversight.”

EthicsFind in source

“Informed consent was obtained from each physician before their participation.”

EthicsFind in source

“This study adhered to the principles outlined in the Declaration of Helsinki.”

EthicsFind in source

WARNReviewer 2· 80% conf

The study states adherence to the Declaration of Helsinki and informed consent for physicians, but lacks a named IRB approval and incorrectly claims the data fall outside IRB oversight.

Evidence

“This study adhered to the principles outlined in the Declaration of Helsinki. Informed consent was obtained from each physician before their participation.”

EthicsFind in source

absence[Methods, Ethics approval]

No named IRB approval is provided.

“This study used only retrospective, de-identified data that fell outside the scope of institutional review board oversight.”

EthicsFind in source

AMIE is described as built on Gemini 2.0 Flash, and the inference process is detailed in supplementary. The clinical data are described. No antibodies, cell lines, or organisms are used. Reagents are not applicable. Software tools (Python version) are identified. The investigational product is adequately described.

Sub-criteria

mycoplasma testingN/A

reagents identifiedN/A

organisms identifiedN/A

antibodies identifiedN/A

cell line authenticationN/A

software tools identifiedADEQUATE

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 85% conf

The investigational product (AMIE) is described, and software tools are identified; no wet-lab reagents are used.

Evidence

“AMIE was built on top of Gemini 2.0 Flash without any additional domain-specific fine-tuning.”

MethodsFind in source

“All analyses were conducted using Python v.2.7.18 ( https://www.python.org/ ).”

Data availabilityFind in source

PASSReviewer 2· 85% conf

The investigational product (AMIE) is described, and the software used for analysis is identified, though version details are incomplete.

Evidence

“AMIE was built on top of Gemini 2.0 Flash without any additional domain-specific fine-tuning.”

MethodsFind in source

“All analyses were conducted using Python v.2.7.18 ( https://www.python.org/ ).”

Data availabilityFind in source

absence[Methods]

No antibodies, cell lines, or organisms are used.

Two-proportion z-tests and McNemar's tests are named. Exact p-values are reported (e.g., P = 0.02). Effect sizes are reported as percentages with differences and CIs (bootstrapped). Software is identified. Data presentation includes proportions with CIs. Mathematical plausibility checks: percentages sum to ~100% in Table 1 (e.g., 20.6+19.6+7.5+10.3+10.3+10.3+19.6 = 98.2, not exactly 100, but likely rounding). No arithmetic errors detected.

Sub-criteria

tests namedADEQUATE

exact p valuesADEQUATE

effect sizes ciADEQUATE

data presentationADEQUATE

software identifiedADEQUATE

assumptions verifiedINADEQUATE

mathematical plausibilityPASS

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 80% conf

Statistical tests are named, p-values are exact, and effect sizes with CIs are reported; assumptions are not explicitly verified but standard methods are used.

Evidence

“we used two-proportion z -tests to compare the selection frequencies of cardiologist + AMIE versus cardiologist alone for each criterion.”

Statistical analysisFind in source

“subspecialists preferring 46.7% of all cases compared to 32.7% for unassisted cardiologists ( P = 0.02).”

ResultsFind in source

“with error bars indicating 95% confidence intervals computed by bootstrapping the scenarios n = 10,000 times.”

Figure 3Find in source

PASSReviewer 2· 80% conf

Statistical tests are named, p-values are exact, and effect sizes with CIs are reported, though some assumptions are not explicitly verified.

Evidence

“we used two-proportion z -tests to compare the selection frequencies of cardiologist + AMIE versus cardiologist alone for each criterion.”

Statistical analysisFind in source

“subspecialists preferring 46.7% of all cases compared to 32.7% for unassisted cardiologists ( P = 0.02).”

ResultsFind in source

“Error bars in Fig. represent 95% confidence intervals derived from bootstrapping ( n = 10,000).”

Statistical analysisFind in source

Data availability statement names a concrete repository (Redivis) with a URL and license. Code availability describes the inference process and Python version, but no public code repository is provided. For a clinical dataset, repository deposit is adequate. Accession numbers are not applicable. Code sharing is partially adequate.

Sub-criteria

code sharingINADEQUATE

accession numbersN/A

repository depositADEQUATE

data availability statementADEQUATE

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 90% conf

Data are openly available with a URL and license, and code is described; no accession numbers are needed.

Evidence

“All data are open-source and are available at https://redivis.com/datasets/1z3x-2354972da?v=next . Data are licensed under open-source license CC 4.0”

Data availabilityFind in source

“The inference process used to generate assessments is detailed in Supplementary Section , while the prompt used in the conversational interface is presented in Supplementary Section .”

Data availabilityFind in source

PASSReviewer 2· 90% conf

Data are openly available with a URL and license, and code availability is described, though the code repository is not explicitly linked.

Evidence

“All data are open-source and are available at https://redivis.com/datasets/1z3x-2354972da?v=next . Data are licensed under open-source license CC 4.0 (http://creativecommons.org/licenses/by/4.0/) .”

Data availabilityFind in source

“The inference process used to generate assessments is detailed in Supplementary Section , while the prompt used in the conversational interface is presented in Supplementary Section . All analyses were conducted using Python v.2.7.18 ( https://www.python.org/ ).”

Data availabilityFind in source

Trial registration is provided (NCT06935253). CONSORT guidelines are mentioned. All pre-specified outcomes are reported, including negative results (e.g., no difference in extra content). Limitations are extensively discussed. Conclusions are proportional, acknowledging the need for prospective studies. Funding and COI are disclosed.

Sub-criteria

funding coiADEQUATE

trial registrationADEQUATE

reporting guidelineADEQUATE

methods completenessADEQUATE

all outcomes reportedADEQUATE

limitations discussedADEQUATE

conclusions proportionalADEQUATE

Per reviewer (2) — the votes behind the verdict

PASSReviewer 1· 85% conf

The paper follows CONSORT, is registered, discusses limitations, and reports funding/COI; all outcomes are reported.

Evidence

“is registered at ClinicalTrials.gov ( NCT06935253 (http://clinicaltrials.gov/ct2/show/NCT06935253) )”

Methods ¶1Find in source

“Our RCT followed the CONSORT RCT guidelines”

Methods ¶1Find in source

“Our study contains a number of important limitations, and the findings should be interpreted with appropriate caution and humility.”

Discussion ¶7Find in source

PASSReviewer 2· 85% conf

The paper follows CONSORT, is registered, discusses limitations, and reports funding and COI, though not all outcomes are pre-specified.

Evidence

“Our RCT followed the CONSORT RCT guidelines and is registered at ClinicalTrials.gov ( NCT06935253 (http://clinicaltrials.gov/ct2/show/NCT06935253) ).”

MethodsFind in source

“Our study contains a number of important limitations, and the findings should be interpreted with appropriate caution and humility.”

Discussion ¶7Find in source

“This study was funded by Alphabet Inc. and/or a subsidiary thereof (‘Alphabet’).”

Competing interestsFind in source

Registered (1 ID: ClinicalTrials.gov). Reporting guideline cited: CONSORT.

ClinicalTrials.gov

NCT06935253

Reporting guidelines: CONSORT

Broken references and links

None found

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.

Checked — nothing surfaced.

Checked 45 references by DOI: 36 verified — 9 no DOI (shown, not verified).

NO DOI
Health Workforce Requirements for Universal Health Coverage and the Sustainable Development Goals
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
Challenges of rural cancer care in the united states
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
HCMA recognized centers of excellence
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
Capabilities of gemini models in medicine
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
Why $4.6 billion health records giant epic is betting big on generative AI
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
Epic, microsoft bring GPT-4 to EHRs
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
Use of GPT-4 to diagnose complex clinical cases
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
Can generalist foundation models outcompete special-purpose tuning? Case study in medicine
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).
NO DOI
PediatricsGPT: large language models as Chinese medical assistants for pediatric applications
No DOI in the reference — shown for manual review; not independently verifiable (not a fabrication signal).

2 data/code links checked; 2 live.

datahttps://redivis.com/datasets/1z3x-2354972da?v=nextLIVEHTTP 200
Resolved page looks like data.
codehttps://www.python.org/LIVEHTTP 200
Resolves, but the content could not be matched to the paper.

Copyediting

6 minor

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

No major wording or formatting errors. 6 minor suggestions below.

6 copyedit issues flagged: mostly consistency, clarity, other.

MINORconsistencyAbstract
“P = 0.02”
→ Ensure consistent use of P vs p throughout.
P is capitalized in abstract but lowercase elsewhere.
MINORclarityMethods, Statistical analysis
“we used two-proportion z -tests”
→ Add a space after 'z' for consistency.
Minor formatting.
MINORotherData availability
“Data consists of clinical test text data”
→ Change 'consists' to 'consist' for subject-verb agreement.
Grammar.
MINORconsistencyResults, General cardiologist perspective
“In 91.6% of cases, there were no reported hallucinations, while in 6.5% of cases, a likely clinically significant hallucination was observed.”
→ Ensure percentages sum to 100% or clarify the remaining 1.9%.
The percentages do not sum to 100%.
MINORconsistencyResults, Individual assessment
“AMIE did not miss anything in 93.4% of cases.”
→ Check consistency with earlier 93.5% figure.
Earlier text states 93.5%.
MINORtypoCode availability
“Python v.2.7.18”
→ Likely a typo; should be Python 3.x.
Python 2.7 is outdated.

Kaimen Rigor Action ItemsFinal pass before journal submission

The published paper is robust to informed reading: the core design and analysis are sound and transparently reported. An informed reader should weigh the following: (1) the absence of a named IRB and the assertion that the data fall outside IRB oversight is a regulatory determination that is not documented; (2) sex is not reported; (3) no power analysis is reported; (4) the code repository is not public; (5) the trial was registered after the study start date (retrospective registration). None of these invalidate the findings, but items (1) and (5) would warrant an erratum or a note from the authors, and items (2)-(4) are reporting gaps an informed reader should weigh.

1.
HIGHethics
Add a named IRB or ethics committee approval with a protocol number to the Ethics approval section, or document the formal determination that the data fell outside IRB oversight.
The current statement asserts the data fall outside IRB oversight without documenting the determination; a named IRB or a documented exemption determination is the standard for human-subjects research.
2.
HIGHreporting
Report the sex distribution of the patient cohort in Table 1 or the Results section.
Sex is a core biological variable in a human clinical study; its omission is a reporting gap that prevents assessment of sex-based effects.
3.
HIGHreporting
Add a power analysis or sample-size justification to the Methods section.
No power analysis is reported; a pilot with a fixed sample still warrants a justification of the sample size.
4.
HIGHdata code
Provide a public link to the analysis code repository (e.g., GitHub) in the Code availability section.
The code is described but not deposited; a public repository would make the analysis reproducible.
5.
HIGHreporting
Clarify whether the trial was registered prospectively or retrospectively, and note the registration date relative to the study start date.
The registration (NCT06935253) appears to have been submitted after the study start date (2025-01-10); prospective vs retrospective registration is a material reporting detail.
6.
MEDIUMstatistics
State the statistical assumptions for the two-proportion z-test and McNemar's test, and how they were verified.
Assumptions are not explicitly verified; stating them improves reproducibility.
7.
MEDIUMdata code
Correct the Python version in the Code availability section (likely Python 3.x, not 2.7.18).
Python 2.7 is end-of-life; the version is likely a typo.
8.
MEDIUMreporting
State the inclusion/exclusion criteria for patient selection explicitly in the Methods section.
The criteria are implicit (consecutive patients); explicit criteria improve reproducibility.
9.
MEDIUMreporting
Reconcile the 93.4% vs 93.5% discrepancy for the same metric (AMIE did not miss anything).
The paper reports 93.4% in one place and 93.5% in another for the same result; the numbers should be consistent.
10.
MEDIUMreporting
Clarify the 1.9% of cases not accounted for in the hallucination percentages (91.6% + 6.5% = 98.1%).
The percentages do not sum to 100%; the residual 1.9% should be explained or the numbers corrected.
11.
LOWcopyedit
Fix the subject-verb agreement in the Data availability section ('Data consists of...' → 'Data consist of...').
Minor grammar issue flagged by the copyedit pass.
12.
LOWcopyedit
Add a space in 'two-proportion z-tests' for consistency.
Minor formatting consistency.
13.
LOWcopyedit
Ensure consistent capitalization of 'P' vs 'p' for p-values across the manuscript.
The abstract uses 'P' while the text uses 'p'; standardize.

Full text (54,385 chars)Analyzed: Aug 15, 2026

RigorReviewer 1

RigorReviewer 2

RigorPre-Submission Reviewer

CopyeditAgent Scribe

CitationsCitation Integrity

StatisticsStatistics Consistency

ReproducibilityLink verification (liveness + content)· no model (rule-based)

PreregistrationDeterministic check· no model (rule-based)

The star rating is the report’s one-glance summary. Every paper starts at 5★ and loses stars for the concrete problems the review finds — so a rating is never a vague average, it’s a running total you can read line by line under “How this rating was calculated.”

How stars are deducted

Reporting — 8 dimensionseach dimension that fully fails−½★
each dimension partially met−¼★
Statistics · Integrity · Claimseach serious problem−1★
each medium problem−½★
Citationseach retracted or unverifiable reference−¼★
Copyeditonly when the manuscript needs a full edit−½★

The rating never drops below 1★, and a demonstrable critical failure (an impossible statistic, a proven ethics violation) caps it at 1★ on its own — so the stars can never look healthy when the verdict is CRITICAL.

The rating draws on a panel of agents. Three independent Kaimen Rigor reviewers grade the eight dimensions below across several independent passes (the shown verdict is their majority vote — steadier than any single run), isolate the paper’s major claims and check its own evidence backs them, and flag integrity concerns. Alongside them, a citation agent resolves every reference against Crossref, OpenAlex, and Retraction Watch; a statistics agent recomputes reported tests; and rule-based checks verify that declared data/code links actually resolve. Full text is required — an abstract-only submission is not analyzed.

The eight reporting dimensions

Scientific PremisePrior work, rationale, and how it addresses gaps.

Study Design & RigorRandomization, blinding, power, controls, replication.

Biological VariablesSex, age, species/strain, housing, demographics.

Ethical ApprovalsIRB/IACUC approval, informed consent, regulatory compliance.

Key ResourcesAntibodies, cell lines, organisms, software — identified with persistent IDs.

Statistical ReportingNamed tests, exact p-values, effect sizes, plausible summary stats.

Data & Code AvailabilityAvailability statements, repositories, accession numbers, shared code.

Reporting TransparencyMethods, registration, guidelines, outcomes, limitations, funding/COI.

Graded against NIH, MDAR, ARRIVE 2.0, CONSORT, EQUATOR, and RRID guidelines. A dimension that doesn’t apply to the study type is skipped, never penalized.

This Kaimen Rigor review is model-assisted and is not a substitute for formal expert review. It complements human evaluation by surfacing potential methodological concerns — verify each finding against the source.

A large language model for complex cardiology care.

DOI

10.1038/s41591-025-04190-9

Record issued

2026-08-15

Engine

7.39.0

Exported

2026-09-20