Prevalence of electrocardiographic markers associated with myocardial fibrosis in masters athletes: a cohort study
Bibliographic record
Abstract
Objectives: Myocardial fibrosis (MF) is associated with an increased predisposition to adverse cardiac events. The accumulation of high-volume and high-intensity exercise over an extended duration potentially increases the risk of MF. Specific electrocardiographic markers have been correlated with the presence of MF. This study assessed the prevalence of MF-related electrocardiographic markers in a Track and Field Master Athletics Cohort (TaFMAC). Methods: Twelve-lead resting electrocardiograms (ECGs) were conducted on 155 athletes (90 males and 65 females) participating in the World Masters Athletics 2022. The ECG markers associated with MF, including pathological Q waves, inverted T waves, fragmented QRS complex, and prolonged QRS complex, were compared among different athletic specialities (endurance athletes n=51, sprinters n=69 and strength and power n=35). Results: Overall, 71 instances of MF-related markers were identified from 155 ECG recordings (46%). Fragmented QRS emerged as the most common marker, with a prevalence of 29% in endurance and strength and power athletes, and 35% in sprinters. No significant group differences were observed in the prevalence of MF markers, whether analysed collectively (p=0.467) or individually (pathological Q waves p=0.367, inverted T waves p=0.309, fragmented QRS complex p=0.747 and prolonged QRS complex p=0.132). Conclusions: The prevalence of MF markers, as determined by resting ECG, was evident in nearly half of masters athletes, irrespective of sex and sporting specialisation. These findings suggest resting ECG as a promising non-invasive method for the early identification of MF in athlete's hearts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".