Arthroscopic Remplissage With Bankart Repair for the Treatment of Glenohumeral Instability With Hill‐Sachs Defects
Bibliographic record
Abstract
PURPOSE: To determine whether arthroscopic remplissage with Bankart repair is an effective treatment strategy for patients with Bankart lesions and large Hill-Sachs defects. METHODS: Between 2006 and 2008, 20 patients underwent arthroscopic Bankart repair with remplissage for the treatment of recurrent anterior glenohumeral instability and large Hill-Sachs defects. Preoperative imaging in all patients identified avulsion of the anterior inferior glenohumeral ligament with an associated Hill-Sachs defect that involved greater than 25% of the humeral head. Patients were followed up postoperatively with the Western Ontario Shoulder Instability score, the American Shoulder and Elbow Surgeons score, and the Penn Shoulder Score. Recurrent subluxation or dislocation was documented. RESULTS: There were 15 male patients and 5 female patients. The mean age of the patients was 27.3 years. The mean length of follow-up in this series was 29.2 months (range, 24.3 to 37.7 months). At final follow-up, 3 patients reported recurrence of instability, which spontaneously reduced in all cases. The mean American Shoulder and Elbow Surgeons score was 92.5 (pain, 47.3; function, 45.3). The mean Penn score was 90 of 100 (pain, 27.3 of 30; satisfaction, 8.5 of 10; function, 54.3 of 60). The mean total Western Ontario Shoulder Instability score was 72.74% (mean physical symptom score, 77.10%; mean sports and recreation score, 70.25%; mean lifestyle score, 75%; mean emotions score, 58.50%). CONCLUSIONS: Using an all-arthroscopic remplissage technique with Bankart repair, we were able to restore function, diminish pain, and yield satisfaction in 85% of the patients in this study. Our results compare favorably with historic controls with similar pathology at early- to intermediate-term follow-up in terms of recurrence. LEVEL OF EVIDENCE: Level IV, therapeutic case series.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".