Prevalence and mechanisms of injuries in water polo: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To summarise the information available in the literature on the prevalence of injuries in water polo and injury risk factors. METHODS: Protocol was registered on Open Science Framework. MEDLINE, CINAHL, Embase and SPORTDiscus databases were searched for keywords relating to water polo and injuries on 3 February 2021. References were searched for additional studies. Only original research papers in English or French were included, and studies without an injured group were excluded. A data extraction file was made based on the Cochrane Collaboration recommendations. Study quality was evaluated with the Newcastle-Ottawa scales for cohorts and a modified version for cross-sectional studies. RESULTS: The initial search yielded 581 articles, with 5 more added from reference lists, but only 41 remained after removing duplicates and applying inclusion/exclusion criteria. Thirty-one articles identified the head, fingers and shoulders as the most common sites of injury. Ten articles on mechanism of injury focused mainly on the shoulder, with degenerative changes, posture, scapular alignment, strength, flexibility and overhead shooting kinematics as the main injury risk factors. Publication types included cohort studies, cross-sectional studies, and one case series. CONCLUSIONS: Most traumatic injuries affect the hands and the head from unexpected contact with the ball or opponents. Conversely, training injuries seem to affect mainly the shoulder area. Low level evidence suggests a correlation between shoulder injuries and lack of strength or flexibility as well as large volumes of overhead throwing. Further prospective research is needed to investigate risk factors for other body areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".