Return to Sport After Surgical Management of Posterior Shoulder Instability: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background:Posterior shoulder instability accounts for a small proportion of all shoulder instability, although it can affect athletes of all types, from contact to overhead athletes. Surgical treatment is quite successful in these patients; however, the literature reports a wide range of rates of return to sport.Purpose/Hypothesis:The purpose was to determine the return-to-sport rates after surgical stabilization for posterior shoulder instability. It was hypothesized that patients would experience a high rate of return to sport.Study Design:Systematic review and meta-analysis; Level of evidence, 4.Methods:Embase, PubMed, and MEDLINE were searched for relevant literature from database inception until April 2020, and studies were screened by 2 reviewers independently and in duplicate for studies reporting rates of return to sport after surgical management of posterior shoulder instability. Demographic data as well as data on return to sport and functional outcomes were recorded. A meta-analysis of proportions was used to combine the rates of return to sport using a random effects model. A risk of bias was assessed for all included studies.Results:Overall, 32 studies met inclusion criteria and comprised 1100 patients (1153 shoulders) with a mean age of 22.8 years (range, 11-65) and a mean follow-up of 43.2 months (range, 10-228). The pooled rate of return to any level of sport was 88% (95% CI, 84%-92%; I2 = 68.7%). In addition, the pooled rate of return to the preinjury level was 68% (95% CI, 60%-76%; I2 = 79%). Moreover, the pooled return-to-sport rate for contact athletes was 94% (95% CI, 90%-97%; I2 = 0%), while for throwing athletes it was 88% (95% CI, 83%-92%; I2 = 0%).Conclusion:Surgical management of posterior shoulder instability resulted in a high rate of return to sport, as well as significant pain reduction and functional improvement in most patients. However, only two-thirds of athletes can return to their preinjury levels of sport.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".