Two-year follow-up of arthroscopic bone block technique with iliac crest autograft and remplissage in patients with anterior shoulder instability and glenoid bone loss
Bibliographic record
Abstract
Background: To retrospectively evaluate patients undergoing arthroscopic bone-block surgery combined with remplissage for anterior shoulder instability with glenoid bone loss, with a minimum follow-up of 2 years. The study assessed graft positioning, osteointegration, resorption, and functional outcomes. Methods: Patients treated between 2019 and 2023 were retrospectively analyzed. Inclusion criteria included: unidirectional anterior shoulder instability, glenoid bone loss between 10% and 30%, and minimum 2-year follow-up. Exclusion criteria were posterior or multidirectional instability, prior bone-block surgery, or glenoid dysplasia. Preoperative computed tomography scans measured glenoid defects and Hill-Sachs lesions. Postoperative and follow-up computed tomography assessed graft positioning, glenoid index, and resorption. Functional outcomes were measured with pre- and postoperative Constant and Western Ontario Shoulder Instability Index scores. Results: < .05), stabilizing at 27.9 ± 2.97 mm at 2 years. The glenoid index improved from 0.76 to 1.13 postoperatively, then stabilized at 0.94. Mean graft resorption was 50.51% ± 22.64%; consolidation was achieved in 96.42%. Functional scores significantly improved: Constant score increased from 63.2 ± 9.1 to 87.74 ± 6.3; Western Ontario Shoulder Instability Index score from 1,220.4 ± 380.7 to 394.28 ± 314.5 (81.21%). One patient had recurrence requiring revision. All returned to sports, including the revision case. Conclusion: Arthroscopic bone-block with iliac crest autograft and remplissage is effective for treating anterior shoulder instability with glenoid bone loss. It provides high consolidation rates, significant functional improvement, and low recurrence. Graft resorption does not appear to impair 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 | 0.001 |
| 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".