Consensus statement on the methodology of injury and illness surveillance in FINA (aquatic sports)
Bibliographic record
Abstract
BACKGROUND: Injury and illness surveillance in the aquatic disciplines has been conducted during the FINA World Championships and Olympic Games. The development of an aquatic-specific injury and illness surveillance system will improve the quality of the data collected and the development of preventive measures. Our ultimate objective is to enhance aquatic athlete health and performance. OBJECTIVE: The objective was to refine the injury and illness surveillance protocols to develop aquatic-specific definitions of injury and illness; define aquatic-specific injury location and causation; better describe overuse injuries; regard pre-existing and recurrent injuries; more accurately define aquatic athlete exposures and develop a protocol to capture out-of-competition aquatic athlete health parameters. METHODS: FINA compiled an Injury and Illness Surveillance Expert Working Group comprised of international experts to review the scientific literature in the field. A consensus meeting was convened to provide an opportunity for debate, following which recommendations were collated. RESULTS: Aquatic-specific injury and illness surveillance protocols covering both the in-competition and out-of-competition time periods were developed. Definitions for all relevant variables were outlined, and documentation forms for athletes and for clinicians were proposed. Recommendations for the implementation of an injury and illness surveillance system for FINA are presented. CONCLUSION: The FINA consensus authors recommend ongoing in-competition and out-of-competition surveillance to determine injury and illness trends over time. The implementation of the definitions and methodology outlined in this paper will improve the accuracy and value of injury and illness surveillance, and provide important information for injury prevention.
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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 | 0.323 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.017 | 0.012 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".