Cardiovascular Screening in Physically Active Individuals: Findings from an Ambulatory Cross-Sectional Study
Bibliographic record
Abstract
Physically active individuals, including both recreational and professional athletes, are often presumed to be inherently healthy. This assumption can result in minimal or absent cardiovascular evaluation, despite evidence that sudden cardiac events may occur in previously asymptomatic populations. International experience shows a range of screening strategies: Italy implements systematic ECG-based pre-participation screening, with observed reductions in sudden cardiac death among athletes, whereas questionnaire-based approaches in countries including the United States, Canada, and the Czech Republic have shown limited detection of latent cardiac conditions. These observations highlight the need for objective early detection methods that complement traditional evaluations. An ambulatory cardiovascular screening program was conducted among 96 physically active participants without prior complaints or referrals. Each participant completed a questionnaire on demographics, training type, and chronic conditions. Cardiac assessment was performed using an AI-driven digital auscultation device that automatically detected and analyzed heart murmurs. Participants with detected murmurs were referred to cardiologists for further evaluation; not all had completed full diagnostic testing at the time of reporting. Cardiac murmurs were detected in 20 of 96 participants (20.8%; 95% CI: 12.7%–28.9%), including both professional and amateur athletes across various training types. The prevalence did not differ significantly between women and men (24.6% vs 15.4%; p=0.28). None of the affected individuals had a prior diagnosis of structural or valvular heart disease, underscoring the limitations of relying solely on history- and questionnaire-based screening. Even asymptomatic, physically active individuals may harbor latent cardiovascular abnormalities, which are often missed by routine screening approaches. Minimal objective evaluation can improve early detection and preventive care in sports medicine.
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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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| 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".