Analysis of water polo injuries during 8904 player matches at FINA World Championships and Olympic games to make the sport safer
Bibliographic record
Abstract
OBJECTIVE: To analyse injuries of water polo players reported during four Summer Olympic Games (2004, 2008, 2012, 2016) and four Federation Internationale de Natation World Championships (2009, 2013, 2015, 2017). METHODS: Injuries during training and matches were reported daily by the team physicians and the local medical staff at the sports venues using an established surveillance system. RESULTS: A total of 381 injuries were reported, equivalent to 14.1 injuries per 100 players (95% CI ±1.42). The most frequent diagnoses were laceration (12.7%) and contusion (10.9%) of head, followed by (sub-)luxation/sprain of hand (9.5%) and contusion of trunk (6.5%) or hand (6.2%). More than half of the injuries (57.0%) occurred due to contact with another player. A quarter of the injuries (25.4%) were expected to result in absence from training or match; 10 (2.9%) resulted in an estimated time-loss of 3 or more weeks. About three-quarters of injuries (75.6%) occurred during matches, 86 during training. The incidence of match injuries was on average 56.2 injuries per 1000 match hours (95% CI ±6.74). The incidence of time-loss match injuries (14.7; 95% CI ±3.44) was significantly higher in men than in women. CONCLUSIONS: A critical review of water polo in-competition rules and the implementation of a Fair Play programme may help to mitigate the high incidence of contact injuries incurred during matches. A water polo-specific concussion education programme including recognition, treatment and return to play is recommended. Finally, a prospective injury surveillance programme would help to better define water polo injuries outside of the competition period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".