Challenges of in-competition cardiac screening: lessons from the 12th FINA World Swimming Championships (25 m) Swimmer's Heart project
Bibliographic record
Abstract
Ensuring the health of the elite athlete is embedded in the Olympic Movement Medical Code,1 and a top priority for International Sports Federations (IF).2 With this objective in mind, cardiovascular preparticipation screening (PPS) is now widely advocated.3–5 While seen as a necessary step in the prevention of the often silent conditions associated with sudden cardiac death (SCD), reports documenting its implementation among the various IFs are sparse. Fifty-six per cent of IFs currently implement PPS,2 however, only FIFA has documented their findings regarding the feasibility of such practice. The adaptive response to intensive continuous exercise varies greatly depending on the sport played, and so the determination of normative cardiac values per sport has benefits when interpreting the standardised, sport-unspecific guidelines currently in place. The selection bias among some sports may also lead to a greater incidence of pathology associated with SCD.6 One of the challenges often faced by IF's in implementing a successful screening programme is the global representation of athletes. Historically, it was therefore …
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".