An Updated Review of the Epidemiology of Swimming Injuries
Bibliographic record
Abstract
OBJECTIVE: To carry out a systematic review to update the scientific evidence on the incidence and prevalence of injuries in the swimming discipline, as well as the location, type, and mechanism of the injuries, and to assess whether studies are meeting methodological recommendations for data collection and injury surveillance. TYPE: Analytical-Systematic review. LITERATURE SURVEY: The databases of PubMed and Sportdiscus were used to search for studies that describe the epidemiology of injuries in adult swimmers between 2010 and March 2020. METHODOLOGY: Of the 864 articles identified, 14 studies were finally included in this review. The methodological quality of the studies was analyzed with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) scale and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. SYNTHESIS: The results showed a high prevalence of shoulder, knee, and lower back injuries among swimmers due to overuse. These injuries were mainly short-term tendon muscles; there were reported data differences between genders. CONCLUSIONS: Despite the publication of an injury surveillance single and multisport events document and a consensus on data collection and injury surveillance in swimming, there are huge methodological limitations that do not allow firm conclusions. As such, more epidemiological studies following guidelines for data collection and injury surveillance are needed to establish differences by gender, age group, and swimming stroke.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".