131 – ILIAC CREST ALLOGRAFT FOR RECURRENT ANTERIOR SHOULDER INSTABILITY IN ATHLETES
Bibliographic record
Abstract
Purpose: 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 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) We hypothesized that restoring a near-normal glenoid structure would prevent further dislocations and that osseous union would be achieved Method: All athletes with recurrent anterior shoulder instability and a large glenoid defect who underwent open anterior shoulder stabilization and glenoid reconstruction with iliac crest allograft were prospectively followed over a three year period. Pre-operatively, a detailed history and physical exam was obtained along with radiographs, a CT scan, and magnetic resonance imaging of the affected shoulder. All patients also complete the Simple Shoulder Test (SST) and American Shoulder and Elbow Surgeons (ASES) evaluation forms pre – and post-operatively. A CT scan was again obtained 6 months post-operatively to assess osseous union of the graft, and the patient again when through a physical exam in addition to completing the SST, ASES, and Western Ontario Shoulder Instability Index (WOSI) forms. Results: Nine patients (all male) 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 nine patients had achieved osseous union at six months (88.9%). ASES scores improved from 64.3 to 96.7, and SST scores improved from 66.7 to 100. Average post-operative WOSI scores were 94%. Conclusion: The use of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".