Arthroscopic Revision with Autologous Iliac Crest Bone Graft for Failed Anatomic Glenoid Reconstruction Using Distal Tibia Allograft
Bibliographic record
Abstract
Recurrent instability after anterior shoulder stabilization surgery is not an uncommon complication, with variable rates of recurrences associated with different surgical procedures. The Latarjet procedure continues to be the gold standard in the management of anterior instability with significant glenoid bone loss, although a recent trend toward arthroscopic anatomic glenoid reconstruction (AAGR) with distal tibial allograft has been noted, with excellent short-term results and minimal complication rates. Arthroscopic revision stabilization for failed stabilization procedures is increasingly being performed, although it is technically more challenging than the primary stabilization procedure because of the anatomic rearrangements of the index surgery. In this article, we describe a revision arthroscopic technique for anatomic glenoid reconstruction using iliac crest autograft for a previous failed AAGR procedure secondary to nonunion of the graft. The graft is passed through the Halifax far-medial portal without splitting the subscapularis. Arthroscopic revision in the setting of a failed AAGR procedure is technically easier than after a Latarjet procedure, as the anatomy is relatively undisturbed in the former, facilitating easier identification of anatomic landmarks, accurate graft positioning, and decreased risk of neurovascular injuries. A Bankart capsulolabral repair is performed after graft fixation, making the graft extra-articular and providing additional stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".