Recreational windsurfing-related acute injuries: a narrative review. Part 1: injury epidemiology and a proposal for standardized injury definitions.
Bibliographic record
Abstract
Objective: The purpose of this review was to identify the epidemiology of, and develop standardized injury definitions for, acute injuries among recreational windsurfers. Methods: A literature search was conducted from the PubMed and Google Scholar databases through February 28, 2023, using relevant keywords with Boolean operators, such as "windsurfing" AND "epidemiology" AND "risk factors." Only peer-reviewed, relevant windsurfing-related injury articles were included. Results: A wide range of acute injuries, from minor, moderate, severe, to catastrophic, were reported. Injury rates, frequency of anatomical distributions, existing and potential risk factors, the proposed standardization definitions of behaviour types, skill levels, general windsurfing-related injuries, and injury severity classifications and levels for windsurfing epidemiology were identified and tabled. Conclusions: There is inconsistency in the epidemiological methods and definitions of windsurfing research. The injury rates remain difficult to compare among the identified studies. Future in-depth windsurfing-related injury studies should focus on prospective designs using standardized injury definitions.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| 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) | 0.002 | 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".