APPROACH TO SHOULDER INSTABILITY: A RANDOMIZED, CONTROLLED TRIAL
Bibliographic record
Abstract
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 (ISIS) is a point-based algorithm that may be used to assist clinicians in selecting the optimal treatment approach, but its efficacy has not been previously validated in a controlled study. We aim to compare two surgical treatment algorithms: the ISIS Score and a conventional treatment algorithm (CTA). This was a prospective, randomized controlled trial involving 63 participants who underwent a shoulder stabilization procedure. Participants were randomized to either the ISIS or CTA and were followed for 24-months post-randomization. Mean age was 31.8 years (range 18-62) and 66% of the cohort was male. The primary outcome was the Western Ontario Stability Index (WOSI). Secondary outcomes included the American Shoulder and Elbow Surgeons (ASES) score as well as recurrence rates between groups. Sixty-three patients were randomized to ISIS (n=33) or CTA (n=30). The WOSI score was similar for the ISIS 84.3±21 and CTA 83.4±14.5 groups (p=0.89). Similarly, no differences were detected between the ASES (90±22.4 and 91.2±11.2 for the ISIS and CTA groups respectively (p=0.87). Apprehension was reported in 10% of participants in each group (p=1.00). At 24-months follow up, there were no re-dislocations in either group. There was one revision surgery in the ISIS group due to graft failure and two in the CTA group. No statistically significant differences in functional outcomes, the incidence of apprehension, or failure rates were identified 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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".