Masters Athlete Screening Study (MASS): Insights Into the Psychological Impact of Cardiovascular Preparticipation Screening
Bibliographic record
Abstract
OBJECTIVE: To determine the psychological impact of a cardiovascular disease (CVD) diagnosis identified during preparticipation screening (PPS) of masters athletes. DESIGN: Cross-sectional study. SETTING: Masters athletes diagnosed with CVD through the Masters Athletes Screening Study. PARTICIPANTS: Sixty-seven athletes (89.6% male, mean age at diagnosis 60.1 ± 7.1 years, range 40-76) with diagnoses of coronary artery disease (CAD) (73.1%), high premature ventricular contraction burden (9.0%), mitral valve prolapse (7.5%), atrial fibrillation (AF) (3.0%), bicuspid aortic valve (3.0%), aortic dilatation (1.5%), coronary anomaly (1.5%), and rheumatic heart disease (1.5%). Three participants had multiple diagnoses. INTERVENTION: Online survey distributed to masters athletes identified with CVD. MAIN OUTCOME MEASURES: Assessment of psychological distress [Impact of Event Scale-Revised (IES-R)], perceptions of screening, and preferred support by CVD type. RESULTS: The median total IES-R and subscale scores were within the normal range {median [interquartile range (IQR)] total 2.0 [0-6.0]; intrusion 1.0 [0-3.0]; avoidance 0 [0-3.0]; hyperarousal 0 [0-1.0]}. Athletes with bicuspid aortic valve [20.5 (IQR, 4.0-37.0)], AF [7.0 (IQR, 0-14.0)], and severe CAD [5.5 (IQR, 1.0-12.0)] had the highest total IES-R scores. One individual with bicuspid aortic valve reported a significant stress reaction. Ten athletes (14.9%) had scores >12. Ninety-three percent of athletes were satisfied having undergone PPS. Preferred type of support varied by cardiovascular diagnosis. CONCLUSIONS: The majority of masters athletes diagnosed with CVD through PPS do not experience significant levels of psychological distress. Athletes diagnosed with more severe types of CVD should be monitored for psychological distress. Support should be provided through a multidisciplinary and individualized approach.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".