Normal echocardiographic and electrocardiographic indices in young female athletes: a meta-analysis across sex, types of sports, races, and ethnicities
Bibliographic record
Abstract
AIMS: High-intensity physical training induces cardiac remodelling, but data on the female athlete's heart are limited. We aimed to determine the normal echocardiographic (ECHO) and electrocardiographic (ECG) indices in young female athletes, compare them with female non-athletes and male athletes, and evaluate differences between female athletes of different races/ethnicities and types of sports. METHODS AND RESULTS: We searched four databases for studies including female athletes aged 18-35. A meta-analysis compared cardiovascular indices between female athletes and the groups above. We included 41 quantitative studies enrolling 11 956 female athletes, 14 014 male athletes, and 549 female non-athletes. Mean age (95% confidence intervals) in years was 22.4 (21.5-23.3) for female athletes, 23.7 (22.3-25.0) for female non-athletes, and 22.3 (21.3-23.3) for male athletes. Female athletes had higher left ventricular end-diastolic volume index [72.5 (66.3-78.8) vs. 55.4 (46.4-64.7) mL/m², P = 0.001], left ventricular mass index (LVMi) [80.8 (75.9-85.7) vs. 62.1(56.6-67.6) g/m², P < 0.0001], and left atrial volume index [29.2 (25.4-32.9) vs. 22.4 (18.7-26.1) mL/m², P = 0.01] than female non-athletes. The LVMi was 21% higher in male athletes than female athletes [98.3 (89.7-106.9) vs. 81.4 (76.6-86.2) g/m2, P < 0.0001]. Black female athletes had more frequent T-wave inversions on ECG than other races/ethnicities [15.2 (7.3-28.8) % of Black vs. 5.4 (2.9-9.9) % of White vs. 2.6 (0.7-9.9) % of Pacific Islanders vs. 0.7 (0.04-9.7) % of Metis, P = 0.04]. CONCLUSION: We confirmed sex-specific left ventricular remodelling, with more eccentric remodelling in female athletes. Black female athletes showed more T-wave inversions than female athletes of other races/ethnicities. Our aggregate means of ECHO and ECG indices may serve as normative values for young female athletes.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".