Pre-Participation and Follow-Up Screening of Athletes for Endurance Sport
Bibliographic record
Abstract
Physical activity increases life expectancy and sport is a priori not harmful. Exhausted sporting activity (e.g. endurance running, triathlon, cycling or competitive sport) can lead under individual conditions to negative cardiac remodelling (pathological enlargement/function of cardiac cavities/structures) or in worst case to cardiac arrhythmias and sudden cardiac death (SCD). This individually disposition can be genetically determined or behaviourally/environmentally acquired. Overall competitive young male athletes suffer five-fold higher than non-competitive athletes from sudden death and athletes aged over 30 bear a potential for arrhythmias, atrial fibrillation or a 20-fold higher possibility for SCD as female athletes. Patients with diabetes, coronary disease, obesity or hypertension require different special managements. Screening of cardiorespiratory health for sport activities has a lot of faces. Basically there is a need for indicated examinations or possible preventive measures inside or outside of pre-competition screening. The costs of screening compared to expenditure of whole effort for sporting activities are acceptable or even negligible, but of course dependent on national/regional settings. The various causes and possibilities of screening will be discussed in this article as basic suggestion for an open discussion beyond national borders and settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".