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Record W2807071476 · doi:10.1139/apnm-2017-0764

Excessive exercise in endurance athletes: Is atrial fibrillation a possible consequence?

2018· article· en· W2807071476 on OpenAlexaffvenue
Jack M. Goodman, Laura Banks, Kim A. Connelly, Andrew T. Yan, Peter H. Backx, Paul Dorian

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsSt. Michael's HospitalUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsAthletesAtrial fibrillationMedicineEndurance trainingPhysical medicine and rehabilitationCardiologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Moderate physical activity levels are associated with increased longevity and lower risk of atrial fibrillation (AF). However, the relative risk of lone AF is 3-5-fold higher in intensive endurance-trained athletes compared with healthy adults. There is growing concern that "excessive" endurance exercise may promote cardiac remodelling, leading to long-term adverse consequences. The pathogenesis of exercise-induced AF is thought to arise from an interplay of multiple acute and chronic factors, including atrial enlargement, pro-fibrotic tendency, high vagal tone, and genotypic profile, which collectively promote adverse atrial remodelling. Clinical management of athletes with AF, while challenging, can be achieved using various strategies that may allow continued, safe exercise. Based on the overall risk-benefit evidence, it is premature to suggest that excessive exercise is unsafe or should be curtailed. Evidence-based assessment and treatment guidelines are required to ensure optimal and safe exercise among the growing number of endurance athletes with AF.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2018
Admission routes2
Has abstractyes

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