Arthroscopic Glenoid Bone Augmentation Using Iliac Crest Autograft Is Safe and Effective for Anterior Shoulder Instability With Bone Loss
Bibliographic record
Abstract
Purpose To establish a safety profile for an arthroscopic anatomic glenoid reconstruction using autologous iliac crest bone graft to treat shoulder instability with significant bone loss and to evaluate short‐term clinical and radiological outcomes. Methods A retrospective analysis of prospectively collected data was conducted for the patients who were treated for shoulder instability with bone loss using arthroscopic autologous iliac crest bone graft between November 2014 and June 2018. The safety profile was established by detective intraoperative or postoperative complications such as neurovascular injuries, infections, major bleeding, and subluxations. Short‐term clinical and radiologic outcomes also were evaluated. Results Thirteen patients were included in the study. A safety profile was observed, with no occurrence of intraoperative complications, neurovascular injuries, infection, or major bleeding. There were no dislocations or positive apprehension tests on clinical examination postoperatively. Postoperative Western Ontario Shoulder Instability (WOSI) scores were significantly greater than preoperative WOSI scores, with a mean improvement of 35.0 ± 20.2 ( P < .001). Twelve patients (92.3%) received postoperative computed tomography scans, with 11 of 12 patients (91.7%) displaying complete graft union. Conclusions Arthroscopic treatment of shoulder instability with bone loss via autologous iliac crest bone graft is shown to be a safe operative procedure that results in favourable short‐term clinical and radiologic outcomes, with a significant improvement in WOSI scores and high rates of graft union. Although graft resorption was seen in most patients who had postoperative computed tomography imaging, there were no instances of clinical graft failure. Level of Evidence Level IV, therapeutic case series.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".