Suspension fixation of iliac bone grafts under arthroscopy is an effective method for the treatment of unstable bony Bankart disease of the shoulder joint in patients with joint relaxation
Bibliographic record
Abstract
PURPOSE: To evaluate the results of arthroscopic autologous iliac bone graft suspension fixation combined with the Remplissage procedure in the treatment of recurrent shoulder dislocation with bony Bankart lesions and joint hyperlaxity. METHODS: From 2018 to 2020, 22 patients with joint laxity underwent arthroscopic autologous iliac bone graft suspension fixation and Bankart repair combined with the Remplissage procedure due to recurrent shoulder dislocation. Clinical assessment included range of motion (forward flexion, abduction, 90° external rotation, conventional external rotation, adduction, and internal rotation), visual analog scale (VAS) score, Rowe score, University of California Los Angeles (UCLA) score, and Western Ontario Shoulder Instability Index (WOSI) score. Post-operatively, the healing of the bone graft was evaluated with computed tomography (CT) scanning. RESULTS: All 22 patients were followed up for a mean of 19.3 ± 4.1 months. CT imaging showed that the healing time of the bone graft was 6-8 weeks. The patient satisfaction rate was 100%, there were no cases of redislocation, all patients returned to their preinjury training state, and the fear test was negative. At the final follow-up, the UCLA, VAS, Rowe, and WOSI scores were 29.8 ± 2.1, 2.2 ± 0.8, 89.4 ± 4.2, and 482.3 ± 46.2, respectively (p < 0.001). CONCLUSION: Arthroscopic autologous iliac bone graft suspension fixation and Bankart repair combined with the Remplissage procedure are effective in preventing recurrent instability with joint hyperlaxity. Furthermore, no patient had redislocation. LEVEL OF EVIDENCE: IV.
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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) | 0.001 | 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".