Atrial fibrillation in middle-aged athletes: Impact on left atrial, ventricular and exercise performance
Bibliographic record
Abstract
High volume endurance training may increase the risk of paroxysmal atrial fibrillation (AF) in middle-aged athletes. Limited data are available describing the cardiovascular phenotype of middle-aged endurance athletes, or the impact of AF on atrial function and exercise performance performed in sinus rhythm. The purpose of this study was to characterize LA phasic function at rest and during exercise in athletes with paroxysmal AF, and to determine its impact on exercise performance. Fifteen endurance trained males (EA) (56 ± 5 years) without AF and 14 endurance trained males with paroxysmal AF (EA-AF) (55 ± 8 years) underwent echocardiography during cycle-ergometry at light and moderate intensities. Resting LA maximal volumes were similar between EA and EA-AF (30 ± 4 vs. 29 ± 8 ml/m2, p = 0.50), and there were no differences in atrial electromechanical delay (AEMD). During moderate intensity exercise, EA-AF had reduced LA conduit (30 ± 6 vs. 40 ± 5 ml/m2, p = 0.002) LA booster volumes (17 ± 5 vs. 21 ± 4 ml/m2, p = 0.021), and reduced LV stroke volumes (100 ± 12 vs. 117 ± 16 ml, p = 0.007). These results demonstrate that exercise testing in athletes with AF unmasks evidence of adverse functional cardiac remodelling that may contribute to impaired exercise performance. It is unclear whether these functional alterations are the consequence of AF. Reductions in LA conduit volume, LA booster volume, and LV stroke volume during exercise may be helpful in clinical management and distinguishing pathologic from physiologic remodelling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".