The Learning Curve for the Latarjet Procedure: A Systematic Review
Bibliographic record
Abstract
Background: Anterior shoulder instability, including recurrent instability, is a common problem, particularly in young, active patients and contact athletes. The Latarjet procedure is a common procedure to treat recurrent shoulder instability. Purpose: To identify the reported learning curves associated with the Latarjet procedure and to determine a point on the learning curve after which a surgeon can be considered to have achieved proficiency. Study Design: Systematic review; Level of evidence, 4. Methods: Three online databases (Embase, MEDLINE, PubMed) were systematically searched and screened in duplicate by 2 independent reviewers. The search included results from the inception of each database to January 23, 2017. Data regarding study characteristics, patient demographics, learning curve analyses, and complications were collected. Study quality was assessed in duplicate. Results: Two level 3 studies and 3 level 4 studies of fair methodological quality were included. Overall, 349 patients (350 shoulders) with a mean age of 25.1 years (range, 14-52 years) were included in the final data analysis. Patients were predominantly male (93.7%). After 22 open and 20 to 40 arthroscopic Latarjet procedures, surgeons achieved a level of proficiency as measured by decreased operative time. For open procedures, complication rates and lengths of hospital stay decreased significantly with increased experience (Spearman ρ = –0.3, P = .009 and Spearman ρ = –0.6, P < .0001, respectively). Conclusion: With experience, surgeons achieved a level of proficiency in performing arthroscopic and open Latarjet procedures, as measured by decreased operative time, length of hospital stay, and complication rate. The most commonly reported difference was operative time, which was significant across all studies. Overall, the Latarjet procedure is a safe procedure with low complication rates, although further research is required to truly characterize this learning curve.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".