Non-shoulder Injuries in Swimming: A Systematic Review
Bibliographic record
Abstract
Objective: The shoulder is the most common injury in swimming, followed by the knee and spine. The repetitive nature of swimming training may predispose a swimmer to injury. Several risk factors have been previously identified, but the level of evidence and level of certainty that these risk factors predispose a swimmer to injury risk has yet to be evaluated critically in a systematic review. Design: Systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data Sources: Data were obtained through PubMed, Cochrane, and Google Scholar. The database search was limited to articles that were published between January 1980 and December 2019. Eligibility Criteria for Selecting Studies: Level I, II, and III studies were included in this review. All studies must have been conducted in swimmers or swimming, a description of the specific pathology and provide at least one identified risk factor with an association (P < 0.05). Results: A total of 19 critically appraised articles identified 28 potential risk factors for musculoskeletal injuries in swimmers. The risk factors were grouped by the anatomical region: neck and back, pelvis and hip, and the knee. Only 2 risk factors were appraised at a moderate level of certainty, both occurring in the knee. The remaining 26 identified risk factors were appraised at a low level of certainty. There is a clear lack of research surrounding nonshoulder injuries in swimmers.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".