Pre-Participation and Follow-Up Screening of Athletes for Endurance Sport
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it