The Arthroscopic Latarjet Procedure for Anterior Shoulder Instability
Bibliographic record
Abstract
BACKGROUND: The arthroscopic Latarjet procedure combines the benefits of arthroscopic surgery with the low rate of recurrent instability associated with the Latarjet procedure. Only short-term outcomes after arthroscopic Latarjet procedure have been reported. PURPOSE: To evaluate the rate of recurrent instability and patient outcomes a minimum of 5 years after stabilization performed with the arthroscopic Latarjet procedure. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Patients who underwent the arthroscopic Latarjet procedure before June 2008 completed a questionnaire to determine whether they had experienced a dislocation, subluxation, or further surgery. The patients also completed the Western Ontario Shoulder Instability Index (WOSI). RESULTS: A total of 62 of 87 patients (64/89 shoulders) were contacted for follow-up. Mean follow-up time was 76.4 months (range, 61.2-100.7 months). No patients had reported a dislocation since their surgery. One patient reported having subluxations since the surgery. Thus, 1 patient (1.59%) had recurrent instability after the procedure. The mean ± standard deviation aggregate WOSI score was 90.6% ± 9.4%. Mean WOSI domain scores were as follows: Physical Symptoms, 90.1% ± 8.7%; Sports/Recreation/Work, 90.3% ± 12.9%; Lifestyle, 93.7% ± 9.8%; and Emotions, 88.7% ± 17.3%. CONCLUSION: The rate of recurrent instability after arthroscopic Latarjet procedure is low in this series of patients with a minimum 5-year follow-up. Patient outcomes as measured by the WOSI are good.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".