Arthroscopic Treatment of Shoulder Instability with Glenoid Bone Loss Using Distal Tibia Allograft Augmentation
Bibliographic record
Abstract
Objectives: The purpose of this study was to retrospectively analyze prospectively collected data to present the clinical and radiological short term outcomes of patients who underwent anatomic glenoid reconstruction using distal tibia allograft to treat shoulder instability with glenoid bone loss. Methods: Over four years, 44 patients (31 patients were male and 13 female with mean age of 29.73 years) underwent arthroscopic stabilization with capsulelabral Bankart repair and allograft bony augmentation of the glenoid for recurrent shoulder instability with significant bone loss by the same surgeon. 14 patients were revision cases of previous surgery. Preoperative and postoperative functional assessment was performed with the Western Ontario Shoulder Instability Index (WOSI) questionnaire, and radiological assessment was performed with radiographs and CT scans. The Average follow-up was 2 years. Results: 97% (43/44) patients had no dislocations or subluxations at the most recent followup. The mean pre and postoperative WOSI scores were 40.54 and 72.65 respectively (p<0.001). No patients developed nerve injury. One patient presented with hardware failure at 3 years post-op. Two other patients had graft absorption and 6 patients had partial graft resorption but none had symptoms of instability. The mean postoperative active shoulder range of motion was forward flexion 170.1o, abduction 168.9o, internal rotation 69.5o and external rotation 57.5o. Grafts positioning was flush with the glenoid in 93% of cases, vertical positioning was excellent in 89% (35 o’clock). Conclusion: Arthroscopic stabilization of the shoulder with distal tibia allograft augmentation is a good safety profile technique with good results at average of two years follow up.
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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".