Electrocardiographic Findings in Professional Male Athletes
Bibliographic record
Abstract
OBJECTIVE: To generate granular normative electrocardiogram (ECG) data and characterize the variation by ethnicity and sport played. DESIGN: Cross-sectional study; ECGs were collected from preseason screening programs from 2012 to 2019 and interpreted in accordance with athlete-specific guidelines. SETTING: Of the professional athletes based in Vancouver, the North Shore Heart Centre performs the annual preparticipation ECG screening. PARTICIPANTS: Seven hundred fifty-three professional male athletes competing in hockey, football, lacrosse, skiing, and snowboarding. INTERVENTIONS: Independent variables include commonly transcribed electrocardiographic findings, for example, those indicating benign and pathologic findings. MAIN OUTCOME MEASURES: Prevalence of and variance in electrocardiographic findings by sport played and ethnicity. RESULTS: Of the 753 athletes, 171 (22.3%) were National Hockey League, 358 (47.5%) were Canadian Football League, 163 (21.6%) were Major League Soccer and/or the Canadian National Soccer team, and 61 (8%) others. The most common finding, sinus bradycardia, was more likely in both soccer (P < 0.001) and hockey (P < 0.001) versus football players. Early repolarization (ER) was more likely in soccer players versus both hockey (P < 0.001) and football players (P = 0.001). Within football, Black athletes (BA) were more likely than White athletes to display ER (P = 0.009), left ventricular hypertrophy (P = 0.004), and nonspecific ST changes (P = 0.027). CONCLUSION: Our study contributes to the generation of normative data for ECG findings while accounting for ethnic and sport-specific variation. The expected clinical presentation of endurance athletes, including soccer players, and the possible predisposition of BA to develop distinct adaptations can augment clinical care by delineating physiology from pathologic changes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".