Return to Sport and Clinical Outcomes After Surgical Management of Acromioclavicular Joint Dislocation: A Systematic Review
Bibliographic record
Abstract
Purpose To evaluate the rate at which athletes return to sport after surgical management of acute and chronic acromioclavicular (AC) joint dislocations. Methods Three databases—PubMed, MEDLINE, and EMBASE—were searched from database inception until October 28, 2017, by 2 reviewers independently and in duplicate. The inclusion criteria were English language studies that reported return to sport outcomes in patients undergoing surgical management of AC joint dislocations. Results Overall, 12 studies with a combined total of 315 patients met the inclusion criteria, with a mean age of 33.8 years (range, 18‐65 years) and a mean follow‐up of 34.9 months (range, 6‐126 months). Of the 12 included studies, 1 was a prospective comparative study (Level II), 1 was a retrospective comparative study (Level III), 1 was a prospective case series (Level IV), and 9 were retrospective case series (Level IV). The rates of return to any level of sport ranged from 94% to 100% (I 2 = 0%), whereas the rates of return to the preinjury level of sport ranged from 62% to 100% (I 2 = 61%). The pooled rate of return to preinjury level of sport in type V AC joint separations was 86.2% (95% confidence interval = 68.1%‐98.0%), whereas that after type III or IV AC joint injuries was 89.6% (95% confidence interval = 79.9%‐96.9%). Conclusions An almost perfect rate of return to sport participation after surgical management of AC joint dislocations have been reported, with most returning to their preinjury level of sport. The rates of return to sport were comparable across the different types of injuries and surgical procedures. Level of Evidence Level IV, systematic review of Level II, III, and IV investigations.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".