Arthroscopic Anatomic Glenoid Reconstruction: Analysis of the Learning Curve
Bibliographic record
Abstract
Objectives: This procedure involves the use of distal tibial bone graft to recreate anterior glenoid bone surface with the goal of preventing further dislocations. Recently, an arthroscopic approach has been proposed for this procedure, which uses a similar technique to the Bankart repair. This approach requires one additional medial portal (4 total), for graft placement, and this is established using an insideout technique; it avoids damage to the subscapularis tendon, and preserves the capsule and labrum. By comparison, the Arthroscopic Latarjet technique requires four additional new portals and requires splitting of the subscapularis tendon, as well as excision of the capsule and labrum. This study seeks to (1) identify a learning curve for this procedure, and (2) compare this to the learning curve for Arthroscopic Latarjet. Methods: Fiftyseven cases of surgically treated recurrent anterior shoulder instability were reviewed. All operations were carried out with the patient in a lateral decubitus position. Twentynine patients were managed with the Arthroscopic Latarjet procedure using coracoid bone graft, and 28 were treated with Arthroscopic Anatomic Glenoid Reconstruction using distal tibial bone graft. Procedure start and stop times were recorded and procedure durations calculated. Results: In the case of Arthroscopic Latarjet, the first 14 cases took an average 184 minutes to perform, with the remaining cases in the cohort averaging 116 minutes each in duration. For Arthroscopic Anatomical Glenoid Reconstruction, the first 14 cases took an average of 90 minutes, with the remaining cases averaging 84 minutes each. Conclusion: Arthroscopic Anatomic Glenoid Reconstruction is faster to perform compared to the Arthroscopic Latarjet. Further investigations into the safety and efficacy of this procedure will help determine whether it is a better choice for surgeons looking to learn the skill of boney augmentation for recurrent anterior instability.
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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 | 0.001 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".