Cardiac remodeling in amateur triathletes after 24 weeks of exercise training for a half-Ironman event: a brief report
Bibliographic record
Abstract
BACKGROUND: Triathletes' physiological adaptations to exercise training can have a different impact on cardiac remodeling based on the extreme exercise preparation. Moreover, cardiac remodeling might be different depending on whether triathletes have trained for many years or if they just decided to be more active. Nevertheless, data are limited in amateur endurance athletes and studies about them are key for their safety. Therefore, we investigated the effects of exercise training for a half-ironman on cardiac remodeling. METHODS: A total of 24 amateur athletes underwent a 24-week exercise program and were followed by three-dimensional echocardiography to assess its global impact on cardiac remodeling. Subanalyses were performed based on participants past-training experience (low versus high). RESULTS: We found significant group effects on the right and left ventricle, significant time effect on the right ventricle. No significant interaction effects were observed. We observed significant correlations between the right ventricle, clinical and performance characteristics where the peak power output explained 38% of the variance, while the body surface area, weight and power at the second ventilatory threshold explained 34%, 31% and 30%, respectively. CONCLUSIONS: Changes in cardiac remodeling in response to an exercise program for a half-ironman are not homogeneous across the ventricles and are influenced by participants' past-training experience. This study strengthens our knowledge of extreme exercise training for a half-ironman to further develop better training programs and medical follow-up in amateur triathletes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".