Learning Curve for Arthroscopic Shoulder Latarjet Procedure Shows Shorter Operating Time and Fewer Complications with Experience
Bibliographic record
Abstract
PURPOSE: To evaluate the learning curve of the arthroscopic Latarjet procedure in a consecutive series of 103 shoulders in 102 patients by comparing the early clinical and radiologic outcomes and complications of the first 25 patients with the latter 25 patients. Our hypothesis was that the studied parameters would be enhanced over time. METHODS: A consecutive cohort of 103 shoulders in 102 patients treated with arthroscopic Latarjet procedure was prospectively registered from December 2014 until November 2019. Patients in this cohort represent the first cases of arthroscopic Latarjet for the 2 shoulder surgeons. All patients had a double screw fixation technique. The Western Ontario Shoulder Instability Index (WOSI) score preoperatively and at 1-year follow-up and 3-dimensional computed tomography scans preoperatively, postoperatively, and at 1-year follow-up were prospectively registered. Patient demographics, intraoperative data, complications, and reoperations were all recorded. In total, 85 of 103 shoulders (83%) had complete data sets. Patient demographics, WOSI scores, operating time, complications, satisfaction rate, and radiology scores in the first and last 25 patients were compared to evaluate learning curve. RESULTS: There was longer operating time in the early group compared with the latter (130 vs 105 minutes, P = .001) and number of complications was reduced with experience (16 vs 4, P = .0005). Serious complications requiring a reoperation were 4 (16%) in the early group compared to 1 (4%) in the latter group (P = .157). Clinical results were good with major improvement in WOSI scores and 84 % satisfaction rates in both groups. CONCLUSIONS: Arthroscopic Latarjet was associated with a learning curve where the early group had longer operating time and greater rates of complications. This is a procedure with few serious complications, acceptable surgery time and learning curve. LEVEL OF EVIDENCE: Level III, retrospective comparative observation trial.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".