Revision Arthroscopic Bankart Repair for Anterior Shoulder Instability After a Failed Arthroscopic Soft-Tissue Repair Yields Comparable Failure Rates to Primary Bankart Repair: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The management of recurrent instability after arthroscopic Bankart repair remains challenging. Of the various treatment options, arthroscopic revision repairs are of increasing interest due to improved visualization of pathology and advancements in arthroscopic techniques and instrumentation. PURPOSE: We sought to assess the indications, techniques, outcomes, and complications for patients undergoing revision arthroscopic Bankart repair after a failed index arthroscopic soft-tissue stabilization for anterior shoulder instability. METHODS: We performed a systematic review of studies identified by a search of Medline, Embase, and PubMed. Our search range was from data inception to April 29, 2020. Outcomes include clinical outcomes and rates of complication and revision. The Methodological Index for Non-randomized Studies (MINORS) was used to assess study quality. Data are presented descriptively. RESULTS: Twelve studies were identified, comprising 279 patients (281 shoulders) with a mean age of 26.1 ± 3.8 years and a mean follow-up of 55.7 ± 24.3 months. Patients had improvements in postoperative outcomes (eg, pain and function). The overall complication rate was 29.5%, the most common being recurrent instability (19.9%). CONCLUSION: With significant improvements postoperatively and comparable recurrent instability rates, there exists a potential role in the use of revision arthroscopic Bankart repair where the glenoid bone loss is less than 20%. Clinicians should consider patient history and imaging findings to determine whether a more rigorous stabilization procedure is warranted. Large prospective cohorts with long-term follow-up and improved documentation are required to determine more accurate failure rates.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".