Recreational windsurfing-related acute injuries: a narrative review. Part 2: injury prevention and a proposal for a set of potential prevention strategies with a holistic approach.
Bibliographic record
Abstract
Objective: The purpose of this review was to identify existing prevention strategies for recreational windsurfing-related acute injuries and provide clinicians with a practical overview of current evidence supporting proposed potential prevention strategies. Methods: A literature search was conducted through March 8, 2023, using relevant keywords with Boolean operators, such as "windsurfing" AND "injury prevention" and "windsurfing" AND "exercise interventions," from the PubMed and Google Scholar databases. Only peer-reviewed English-articles were included. Results: Existing prevention strategies, right-of-way rules, a new proposed set of eight potential primary to tertiary prevention strategies for windsurfing-related acute injuries, and proposed definitions of injury prevention levels equivalent to Haddon's matrix were identified and tabled. Conclusions: The proposed potential prevention strategies may facilitate clinicians in preventing recreational windsurfing-related acute injuries. Injury prevention for recreational windsurfing is under-researched. Future studies should focus on large prospective clinical trials evaluating the efficacy of prevention strategies for recreational windsurfing-related injuries.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".