Arthroscopic Anatomic Glenoid Reconstruction in the Unstable Shoulder: Technique, Pearls, and Pitfalls
Bibliographic record
Abstract
Background: Anterior shoulder instability with glenoid bone loss is a complex condition. Bankart repairs have higher failure rate in this population and the Latarjet procedure is associated with a high complication rate (15%-30%). A recent technique, the arthroscopic anatomic glenoid reconstruction, safely uses distal tibial allograft to augment the glenoid. Indications: Glenoid or bipolar bone loss in the setting of shoulder instability. Technique Description: A diagnostic shoulder arthroscopy is performed to assess bone loss and capsulolabral tissue. After the preparation of the anterior glenoid, a bone block harvested from a distal tibial allograft is prepared. This technique uses the Halifax portal, a safe, far medial portal to insert the graft, and compress it onto the anterior glenoid using screws. A Bankart repair is then performed, to reduce the capsulolabral complex onto the glenoid. Results: Results at 2 years show a 92% to 100% union of the graft, no recurrence of instability, and improved patient-reported outcome scores. Graft remodeling is regularly observed on postoperative imaging. This procedure may be faster to learn and to perform compared to an arthroscopic Latarjet. Discussion/Conclusion: Arthroscopic anatomic glenoid reconstruction is a safe, minimally invasive procedure to address shoulder instability. It has low complication rate and is associated with improved patient-reported outcomes. Patient Consent Disclosure Statement: The author(s) attest that consent has been obtained from any patient(s) appearing in this publication. If the individual may be identifiable, the author(s) has included a statement of release or other written form of approval from the patient(s) with this submission for publication.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".