Incidence of Concussions in Elite Female Water Polo: A Retrospective Analysis
Bibliographic record
Abstract
OBJECTIVE: Current evidence for concussions is mixed in water polo players. Surveys suggest rates as high as 36%, whereas surveillance studies at international competitions often fail to report them at all. The goal of this study was to examine the incidence of concussions in elite female water polo players from surveillance tools implemented longitudinally. DESIGN: Retrospective chart analysis of 10 years spanning between 2012 and 2022. SETTING: National teams or professional sports. PARTICIPANTS: Female players from the Canadian senior national water polo teams that participated in international competitions and trained more than 10 hours per week in a competitive environment. ASSESSMENT OF RISK FACTORS: Included player position, recurrence, and time loss before full return to play. MAIN OUTCOME MEASURES: Prevalence of concussion diagnosis. RESULTS: Forty-three concussions were identified over the 10 years observed at a median count of 3 concussions per year. Cumulative days lost spanned between 25 and 348 days per team*year. Altogether, this produced a median rate of 14.3 injuries per 100 player*years. Goalkeepers in the sample suffered the highest rates of concussion (25.8%), compared with players in other positions (22.8% of centers and 16.7% of drivers). CONCLUSIONS: This study found evidence for prevalence of concussions in elite female water polo players. Improved guidelines preceded a significant reduction in time loss after the year 2017. This adds to a body of knowledge suggesting that improved surveillance methods are needed to detect and care for concussions in this population.
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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 | 0.003 |
| 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".