Sudden Cardiac Arrest in Basketball and Soccer Stadiums, the Role of Automated External Defibrillators: A Review. For the BELTRAN Study (BaskEtbaLl and soccer sTadiums: Registry on Automatic exterNal defibrillators)
Bibliographic record
Abstract
Sudden cardiac arrest (SCA) during sports events has a dramatic impact on stadium-goers and the public and is often associated with poor outcomes unless treated with an automated external defibrillator (AED). Despite this, stadiums vary in AED use. This review aims to identify the risks and incidences of SCA, and the use of AEDs in soccer and basketball stadiums. A narrative review of all relevant papers was conducted. Athletes across all sports face an SCA risk of 1:50,000 athlete-years, with the greatest risk of SCA in young male athletes (1:35,000 person-years) and black male athletes (1:18,000 person-years). Africa and South America have the poorest soccer SCA outcomes at 3% and 4% survival. AED use on-site improves survival greater than defibrillation by emergency services. Many stadiums do not have AEDs implemented into medical plans and the AEDs are often unrecognisable or are obstructed. Therefore, AEDs should be used on-site, use clear signalling, have certified trained personnel, and be incorporated into stadiums' medical plans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".