Electrocardiographic and Echocardiographic Insights From a Prospective Registry of Asian Elite Athletes
Bibliographic record
Abstract
Background: Asian representation in sport is increasing, yet there remains a lack of reference values for the Asian athlete's heart. Consequently, current guidelines for cardiovascular screening recommend using Caucasian athletes' norms to evaluate Asian athletes. This study aims to outline electrocardiographic and echocardiographic characteristics of the Asian athlete's heart using a Singaporean prospective registry of Southeast (SE) Asian athletes. Methods and Results: One hundred and fifty elite athletes, mean age of 26.1 ± 5.7 years (50% males, 88% Chinese), were evaluated using a questionnaire, 12-lead electrocardiogram (ECG) and transthoracic echocardiogram. All ECGs were analyzed using the 2017 International Recommendations. Echocardiographic data were presented by gender and sporting discipline. The prevalence of abnormal ECGs among SE Asian athletes was 6.7%—higher than reported figures for Caucasian athletes. The abnormal ECGs comprised mainly anterior T wave inversions (ATWI) beyond lead V2, predominantly in female athletes from mixed/endurance sport (9.3% prevalence amongst females). None had echocardiographic structural abnormalities. Male athletes had reduced global longitudinal strain compared to females (−18.7 ± 1.6 vs. −20.7 ± 2.1%, p < 0.001). Overall, SE Asian athletes had smaller left ventricular cavity sizes and wall thickness compared to non-Asian athletes. Conclusion: SE Asian athletes have higher abnormal ECG rates compared to Caucasian athletes, and also demonstrate structural differences that should be accounted for when interpreting their echocardiograms compared to athletes of other ethnicities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".