The Evaluation and Management of the Failed Primary Arthroscopic Bankart Repair
Bibliographic record
Abstract
Primary arthroscopic Bankart repair is a common procedure that is increasing in popularity; however, failure rates can approach up to 6% to 30%. Factors commonly attributed to failure include repeat trauma, poor or incomplete surgical technique, humeral and/or glenoid bone loss, hyperlaxity, or a failure to identify and address rare pathology such as a humeral avulsion of the glenohumeral ligament lesion. A thorough clinical and radiographic assessment may provide insight into the etiology, which can assist the clinician in making treatment recommendations. Surgical management of a failed primary arthroscopic Bankart repair without bone loss can include revision arthroscopic repair or open repair; however, in the setting of bone loss, the anterior-inferior glenoid can be reconstructed using a coracoid transfer, tricortical iliac crest, or structural allograft, whereas posterolateral humeral head bone loss (the Hill-Sachs defect) can be addressed with remplissage, structural allograft, or partial humeral head implant. In addition to the technical demands of revision stabilization surgery, patient and procedure selection to optimize outcomes can be challenging. This review will focus on the etiology, evaluation, and management of patients after a failed primary arthroscopic Bankart repair, including an evidence-based treatment algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".