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Record W3037292383 · doi:10.1080/14017431.2020.1783458

Observations on changes in ventricular repolarization following four weeks of exercise training in chronic heart failure patients

2020· article· en· W3037292383 on OpenAlexaff
Maxime Caru, Hugo Gravel, Atul Pathak, Marc Bousquet, Michel Galinier, Vincent Jacquemet, Daniel Curnier

Bibliographic record

VenueScandinavian Cardiovascular Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsHôpital du Sacré-Cœur de MontréalUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineHeart failureVentricular RepolarizationCardiologyInternal medicineRepolarizationPhysical therapyElectrophysiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to investigate the effects of exercise training on ventricular repolarization dynamicity and heart rate variability in chronic heart failure patients. DESIGN: A total of 22 chronic heart failure patients with reduced ejection fraction in sinus rhythm were included in the study. The patients were in NYHA classes II-III with an ejection fraction of 29.7 ± 7.7%. Before and after 4 weeks of aerobic exercise training, all patients performed a cardiopulmonary exercise test, a standard twelve-lead electrocardiogram and a 24 h Holter recording from which heart rate variability and ventricular repolarization dynamicity were assessed. RESULTS: < .001) at RR intervals ranging from 600 to 1000 ms on 24 h QT/RR regressions after 4 weeks of exercise training. Our analyses revealed that short-term exercise training induced significant changes in the frequency and time domain HRV parameters on an overall time-period of 24 h. CONCLUSION: Four weeks of exercise training induced significant changes in ventricular repolarization dynamicity in chronic heart failure patients. In addition, short-term exercise training was enough to improve patients' heart rate variability.

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 score0.077
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.237
Teacher spread0.213 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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