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Record W2792757684 · doi:10.1111/echo.13857

E/eʹ ratio and left atrial area are predictors of atrial fibrillation in patients with hypertrophic cardiomyopathy

2018· article· en· W2792757684 on OpenAlexaff
Juan Pablo Costabel, Enrique Galve, María Terricabras, Clara Ametrano, Ricardo Ronderos, Adrián Baranchuk, Arturo Evangelista, Gustavo Avegliano

Bibliographic record

VenueEchocardiography · 2018
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineCardiologyInternal medicineAtrial fibrillationHypertrophic cardiomyopathyUnivariate analysisCardiomyopathyHeart failureRetrospective cohort studyMultivariate analysis

Abstract

fetched live from OpenAlex

INTRODUCTION: Atrial fibrillation (AF) occurs in about 20%-25% of patients with hypertrophic cardiomyopathy and is associated with increased risk of cardioembolism and heart failure impacting on patients' morbidity and mortality. The aim of this study was to identify echocardiographic predictors of AF in a cohort of patients with hypertrophic cardiomyopathy (HCM). METHODS: Patients were recruited from 2 centers: Buenos Aires Cardiovascular Institute and the Hospital Vall d'Hebron of Barcelona which were analyzed together. Retrospective study using electronic charts. RESULTS: A total of 321 patients with HCM and no documented history of AF were included. Median follow-up was 3 years. Mean age was 54 ± 16 years. Obstructive HCM was present in 41% of the patients, and 94.2% had preserved systolic function. Thirty-eight patients developed AF during the follow-up period (11.8%). Univariate analysis showed that age, maximum myocardial thickness, atrial area, an E/e' ratio ≥ 17, and systolic pulmonary pressure estimated by echocardiography were associated with new-onset AF. Multivariate analysis showed that E/e' ≥ 17 ratio {HR 3.27 ([1.10-9.27] P = .033)} and atrial area {HR 1.06 ([1.01-1.13] P = .037)} remained predictors of AF. CONCLUSIONS: are strong predictors of AF in patients with HCM.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.238
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2018
Admission routes1
Has abstractyes

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