Arthroscopic Anatomic Glenoid Reconstruction: Analysis of the Learning Curve
Bibliographic record
Abstract
Background: Anatomic glenoid reconstruction involves the use of distal tibial allograft for bony augmentation of the glenoid surface. An all-arthroscopic approach was recently described to avoid damage to the subscapularis tendon and preserve the capsule and labrum. Purpose: To explore and compare change in surgical time between 2 proposed methods used for the treatment of anterior shoulder instability—arthroscopic anatomic glenoid reconstruction (AAGR) and arthroscopic Latarjet (AL)—over successive procedures. We also compared graft positioning on the anterior glenoid surface between the 2 methods. Study Design: Cohort study; Level of evidence, 3. Methods: This was a single-surgeon retrospective review of 54 cases of surgically treated recurrent anterior shoulder instability: 27 had AAGR with distal tibial allograft, while the other 27 had AL. AAGR with the distal tibial allograft was the primary choice for the treatment of anterior shoulder instability; however, AL was performed when tibial allograft was not available from the bone bank. Thus, there was an overlapping period for those 2 procedures. Procedure start and end times were recorded, and duration was calculated. Postoperative 3-dimensional computed tomography scans were reviewed, and graft position was judged to be in the lower third (desired position), middle third, or upper third of the anterior glenoid surface. To assess learning, these data were organized in chronological order of surgery, and each surgical cohort was divided into 3 chronological clusters of 9 patients each. Learning was assessed through change in operative time over successive clusters, change in variability of operative time among clusters, and change in graft positioning among clusters. Statistical analysis comprised a 2-tailed independent-sample t test and the Levene test for equality of variance. Results: Our study found that AAGR was significantly faster to perform than AL in the early ( P = .001), middle ( P = .001), and late ( P = .05) clusters of each cohort. Duration of surgery did not significantly improve across clusters within each cohort ( P = .15-.79). There were no significant changes in the variability of surgical time in the AAGR group ( P = .09) or the AL group ( P = .13). Desired positioning of the bone graft on the anterior glenoid surface (lower third) was identified more commonly in the AAGR cohort. Conclusion: AAGR is faster to learn and perform than AL for the treatment of recurrent anterior shoulder instability with significant glenoid bone loss. The current study found higher rates of desired graft positioning for AAGR clusters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".