Approach to shoulder instability: a randomized, controlled trial
Bibliographic record
Abstract
Background The significant rate of recurrent instability following arthroscopic stabilization surgery points to a need for an evidence-based treatment approach. The instability severity index Score (ISI score) is a point-based algorithm that may be used to assist clinicians in selecting the optimal treatment approach, but its efficacy compared with a traditional treatment algorithm has not been previously validated. The aim was to compare two surgical treatment algorithms: the ISI score and a conventional treatment algorithm (CTA). Methods This was a prospective, randomized controlled trial involving participants who were randomized to either the ISI score or CTA and were followed for 24 months postrandomization. In the ISI score cohort, patients underwent a Latarjet procedure if they presented with a score >3 points. Those scoring ISI score ≦3 points underwent an arthroscopic Bankart repair. Patients randomized to the CTA group underwent a Latarjet procedure if the glenoid bone loss was > 25%. The primary outcome was the Western Ontario Shoulder Instability Index. Secondary outcomes included the American Shoulder and Elbow Surgeons score as well as recurrence rates between groups. Results Sixty-three patients were randomized to ISI score (n = 31) or CTA (n = 32). At two years, the Western Ontario Shoulder Instability Index score was similar between groups (ISI score: 84.1 ± 16.9, CTA: 85.7 ± 12.5, P = .70). Similarly, no differences were detected in American Shoulder and Elbow Surgeons scores (ISI score: 93.2 ± 16.2, CTA: 92.6 ± 9.9, P = .89). Apprehension was reported in 18.5% for the ISI score group and 20% in the CTA group ( P = 1.00). At a 24-month follow-up, there was no difference in redislocations: one in ISI score group and none in the CTA group ( P = .48). There were two revision surgeries in the ISI score group and two in the CTA group. Conclusion This study did not demonstrate any differences in functional outcomes, the incidence of apprehension, or failure rates between the two treatment algorithms at 24-month follow-up.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".