Revision Arthroscopic Bankart Repair
Bibliographic record
Abstract
PURPOSE: Failed anterior shoulder stabilization procedures have traditionally been treated with open procedures. Recent advances in arthroscopic techniques have allowed for certain failed stabilization procedures to be treated by arthroscopic surgery. The aim of this systematic review was to determine the outcomes of revision arthroscopic Bankart repair. METHODS: We searched Medline, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) for articles on revision arthroscopic Bankart repairs. Key words included shoulder dislocation, anterior shoulder instability, revision surgery, and arthroscopic Bankart repair. Two reviewers selected studies for inclusion, assessed methodologic quality, and extracted data. RESULTS: We included 16 studies comprising 349 patients. All studies were retrospective (1 Level II study and 15 Level IV studies). The mean incidence of recurrent instability after revision arthroscopic Bankart repair was 12.7%, and the mean follow-up period was 35.4 months. The most common cause for failure of the primary surgeries was a traumatic injury (62.1%), and 85.1% of patients returned to playing sports. The reasons for failure of revision cases included glenohumeral bone loss, hyperlaxity, and return to contact sports. CONCLUSIONS: With proper patient selection, the outcomes of revision arthroscopic Bankart repair appear similar to those of revision open Bankart repair. Prospective, randomized clinical trials are required to confirm these findings. LEVEL OF EVIDENCE: Level IV, systematic review of Level II and Level 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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".