Assessment of cardiovascular risk and preparticipation screening protocols in masters athletes: the Masters Athlete Screening Study (MASS): a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Underlying coronary artery disease (CAD) is the primary cause of sudden cardiac death in masters athletes (>35 years). Preparticipation screening may detect cardiovascular disease; however, the optimal screening method is undefined in this population. The Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and the American Heart Association (AHA) Preparticipation Screening Questionnaire are often currently used; however, a more comprehensive risk assessment may be required. We sought to ascertain the cardiovascular risk and to assess the effectiveness of screening tools in masters athletes. METHODS: This cross-sectional study performed preparticipation screening on masters athletes, which included an ECG, the AHA 14-element recommendations and Framingham Risk Score (FRS). If the preparticipation screening was abnormal, further evaluations were performed. The effectiveness of the screening tools was determined by their positive predictive value (PPV). RESULTS: 798 athletes were included in the preparticipation screening analysis (62.7% male, 54.6±9.5 years, range 35-81). The metabolic equivalent task hours per week was 80.8±44.0, and the average physical activity experience was 35.1±14.8 years. Sixty-four per cent underwent additional evaluations. Cardiovascular disease was detected in 11.4%, with CAD (7.9%) being the most common diagnosis. High FRS (>20%) was seen in 8.5% of the study population. Ten athletes were diagnosed with significant CAD; 90% were asymptomatic. A high FRS was most indicative of underlying CAD (PPV 38.2%). CONCLUSION: Masters athletes are not immune to elevated cardiovascular risk and cardiovascular disease. Comprehensive preparticipation screening including an ECG and FRS can detect cardiovascular disease. An exercise stress test should be considered in those with risk factors, regardless of fitness level.
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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 | 0.004 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".