Arthroscopic Laser-Assisted Capsular Shift in the Treatment of Patients with Multidirectional Shoulder Instability
Bibliographic record
Abstract
BACKGROUND: In recent years, various investigators have begun using lasers in the treatment of shoulder instability. HYPOTHESIS: Arthroscopic laser-assisted capsular shift is an effective treatment for patients with multidirectional shoulder instability. STUDY DESIGN: Retrospective cohort study. METHODS: We retrospectively identified 28 patients (30 shoulders) with multidirectional shoulder instability who were unresponsive to nonoperative management and who had undergone the laser-assisted capsular shift procedure. Twenty-five patients (27 shoulders) with an average follow-up of 28 months were available for review. All patients underwent a physical examination and completed a general questionnaire; the University of California, Los Angeles, shoulder rating scale; the Western Ontario Shoulder Instability Index; and the Short-Form 36 quality of life index. RESULTS: In 22 shoulders, results of the procedure were considered a success because the patients had no recurrent symptoms and at latest follow-up had required no further operative intervention. In five shoulders, results were considered a failure because of recurrent pain or instability and the need for an open capsular shift procedure. With recurrent instability as a measure of failure, the overall success rate was 81.5%. CONCLUSIONS: Our results with laser-assisted capsular shift are comparable with the results of other open and arthroscopic techniques in relieving pain and returning athletes to their premorbid function.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".