Joint hypermobility in athletes is associated with shoulder injuries: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Joint hypermobility in athletes is associated with increased risk of knee injuries, but its role in relation to shoulder injuries has not been scrutinized. Therefore, our aim was to synthesize the evidence on the association between joint hypermobility and shoulder injuries in athletes. METHODS: Data sources were MEDLINE, CINAHL, EMBASE, and SPORTDiscus from inception to 27th February 2021. Eligibility criteria were observational studies of athletes (including military personnel), mean age ≥ 16 years, and with a transparent grouping of those with and without joint hypermobility. A broad definition of joint hypermobility as the exposure was accepted (i.e., generalised joint hypermobility (GJH), shoulder joint hypermobility including joint instability). Shoulder injuries included acute and overuse injuries, and self-reported pain was accepted as a proxy for shoulder injuries. The Odds Ratios (OR) for having shoulder injuries in exposed compared with non-exposed athletes were estimated using a random effects meta-analysis. Subgroup analyses were performed to explore the effect of sex, activity type, sports level, study type, risk of bias, and exposure definition. Risk of bias and the overall quality of evidence were assessed using, respectively, the Newcastle-Ottawa Scale and the Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS: = 75.3%; p = 0.001). Exposure definition (GJH, OR = 1.97, 95% CI 1.32, 2.94; shoulder joint hypermobility, OR = 8.23, 95% CI 3.63, 18.66; p = 0.002) and risk of bias (low, OR = 5.25, 95% CI 2.56, 10.8; high, OR = 1.6, 95% CI 0.78, 3.29; p = 0.024) had large impacts on estimates, while the remaining subgroup analyses showed no differences. The overall quality of evidence was low. CONCLUSION: Joint hypermobility in athletes is associated with a threefold higher odds of having shoulder injuries, highlighting the need for prevention strategies in this population. However, due to low quality of evidence, future research will likely change the estimated strength of the association. PROTOCOL REGISTRATION: Open Science Framework registration osf.io/3wrn9.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".