Iliac crest allograft glenoid reconstruction for recurrent anterior shoulder instability in athletes: Surgical technique and results
Bibliographic record
Abstract
Performing a labral repair alone in patients with recurrent anterior instability and a large glenoid defect has led to poor outcomes. We present a technique involving the use of the iliac crest allograft inserted into the glenoid defect in athletes with recurrent anterior shoulder instability and large bony defects of the glenoid (>25% of glenoid diameter). All athletes with recurrent anterior shoulder instability and a large glenoid defect that underwent open anterior shoulder stabilization and glenoid reconstruction with the iliac crest allograft were followed over a 4-year period. Preoperatively, a detailed history and physical exam were obtained along with standard radiographs and magnetic resonance imaging of the affected shoulder. All patients also completed the Simple Shoulder Test (SST) and American Shoulder and Elbow Surgeons (ASES) evaluation forms preoperatively. A computed tomography scan was obtained postoperatively to assess osseous union of the graft and the patient again went through a physical exam in addition to completing the SST, ASES, and Western Ontario Shoulder Instability Index (WOSI) forms. 10 patients (9 males, 1 female) were followed for an average of 16 months (4-36 months) and had a mean age of 24.4 years. All patients exhibited a negative apprehension/relocation test and full shoulder strength at final follow-up. Eight of 10 patients had achieved osseous union at 6 months (80.0%). ASES scores improved from 64.3 to 97.8, and SST scores improved from 66.7 to 100. Average postoperative WOSI scores were 93.8%. The use of the iliac crest allograft provides a safe and clinically useful alternative compared to previously described procedures for recurrent shoulder instability in the face of glenoid deficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".