The Epidemiology of Youth Sport-Related Shoulder Injuries: A Systematic Review
Bibliographic record
Abstract
Background: Youth around the globe place their shoulders at risk for injury when participating in sports. Shoulder injuries may vary in severity, produce the potential for time-loss from sport, and result in functional disability. We sought to explore sport-related shoulder injuries in youth by identifying injury rates, risk factors, injury mechanisms, and injury prevention strategies. Methods: All relevant full-text articles were identified by searching MEDLINE, EMBASE, CINAHL, Sport Discus, and the Cochrane Controlled Trials Registry. No date restrictions were used. All full-text studies reporting original research describing sport-related shoulder injury among female and/or male youth from 5 to 18 years old were included. Results: Of 3,889 studies screened, 97 described shoulder injury in youth sports. Shoulder injuries were identified in 24 unique sports. The median seasonal prevalence of shoulder injury was 10.9% (range 1.2-28.2%). The most common injury mechanisms identified were contacted with another player, contact with the playing environment, and falling to the ground. Risk factors for shoulder injury identified were side-to-side strength imbalances, weak external rotator muscles, and scapular dyskinesia. One study evaluated a successful training strategy to prevent shoulder injuries, but two other interventions demonstrated no effect. Conclusions: Sport-related shoulder injuries are prevalent among youth athletes. Injury risk factors identified included modifiable intrinsic factors such as strength, range of motion, and training load. The most common injury mechanism was direct contact with either another person or an object in the playing environment. Innovative shoulder-specific strategies are needed to reduce shoulder injuries in this population. Trial Registration: PROSPERO ID: CRD42020189142.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".