Return to Sport After Arthroscopic Bankart Repair With Remplissage: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To determine the return-to-sport rate following arthroscopic Bankart repair with remplissage (ABR), including overall rate of return to sport, rate of return to preinjury level of sport, and the rate of return for specific subgroups such as contact or throwing athletes. METHODS: EMBASE, PubMed, and MEDLINE were searched from database inception until February 2022. Studies were screened by 2 reviewers independently and in duplicate for data regarding rates of return to sport following ABR. Data on return to sport and functional outcomes were recorded. Data are presented in a descriptive fashion. RESULTS: Overall, 20 studies were included with a total of 736 patients (738 shoulders) who underwent ABR. These patients had a mean age of 28 years (range 14-72 years) and were 83% male. Mean follow-up time after surgery was 45 months (range 12-127 months). The rate of return to any level of sport ranged from 60% to 100%, whereas the rate of return to the preinjury level ranged from 63% to 100%. When we excluded those who underwent ABR as a revision procedure, the rate of return to any level of sport was 68% to 100%. Lastly, the return to sport rates for contact or collision athletes ranged from 80% to 100%, whereas for overhead or throwing athletes it was 46% to 79%. The rate of recurrence of instability postoperatively ranged from 0% to 20% in included studies. CONCLUSIONS: For athletes with anterior shoulder instability, ABR led to a high rate of return to sport along with a low rate of recurrence of instability. Although most athletes are able to return to the same level of sport, certain groups such as throwing athletes may face greater difficulty. LEVEL OF EVIDENCE: Level IV, systematic review of Level III and IV studies.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 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".