Arthroscopic Anatomic Glenoid Reconstruction Using Distal Tibial Allograft for Recurrent Anterior Shoulder Instability: Clinical and Radiographic Outcomes
Bibliographic record
Abstract
BACKGROUND: The all-arthroscopic anatomic glenoid reconstruction technique using a distal tibial allograft avoids damage to the subscapularis muscle and allows repair of the capsulolabral tissue. PURPOSE: To analyze the clinicoradiologic outcomes of patients who underwent this procedure to treat anterior shoulder instability with glenoid bone loss with a minimum 2-year follow-up. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Over 6 years, 73 patients (52 male and 21 female; mean age, 28.8 years) under the care of the same surgeon underwent arthroscopic stabilization with capsulolabral Bankart repair and bony allograft augmentation of the glenoid for recurrent shoulder instability with significant bone loss. Pre- and postoperative patient-reported functional assessment was performed using 2 questionnaires, the Western Ontario Shoulder Instability Index (WOSI) and the Disabilities of the Arm, Shoulder and Hand, and radiological assessment was performed using radiographs and computed tomography scans obtained preoperatively and approximately 1 year later (mean ± SD, 0.9 ± 1.1 years). RESULTS: < .001). There were no recurrences of dislocation, although 1 patient had symptoms of subluxation; however, 5 patients had hardware complications that required screw removal. There were no cases of nerve injury. Postoperative computed tomography scans were available for 66 patients. Seven patients were lost to follow-up. The graft union rate was 100%. Overall, graft resorption was <50% in 86% of patients (57/66). Eighteen patients (27%) had no resorption (grade 0), 39 (59%) had <50% (grades 1 and 2), and 9 (14%) had ≥50% (grade 3); however, none had symptoms of instability. The mean alpha angle of the screw between the screw shaft axis and the native glenoid axis was 18.3°± 5.7°. Graft positioning was flush with the glenoid in 61 of 66 patients (92.4%), and vertical positioning was excellent in 64 of 66 patients (97.0%) (3- to 5-o'clock position). CONCLUSION: Arthroscopic stabilization using distal tibial allograft augmentation resulted in excellent clinicoradiologic outcomes at a 2-year follow-up. This procedure has the advantages of being an anatomic reconstruction that addresses bony and soft tissue instability. However, long-term follow-up studies are necessary for better assessment of outcomes.
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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".