A lower Instability Severity Index score threshold may better predict recurrent anterior shoulder instability after arthroscopic Bankart repair: a systematic review
Bibliographic record
Abstract
IMPORTANCE: The Instability Severity Index (ISI) score was developed to evaluate a patient's risk of recurrent shoulder instability following arthroscopic Bankart repair. While patients with an ISI score of >6 were originally recommended to undergo an open procedure (ie, Latarjet) to minimise the risk of recurrence, recent literature has called into question the utility of the ISI score. OBJECTIVE: The purpose of this systematic review was to evaluate the efficacy of the ISI score as a tool to predict postoperative recurrence among patients undergoing arthroscopic Bankart procedures. EVIDENCE REVIEW: test was used to compare recurrence rates among patients above and below an ISI score of 4. Sensitivity, specificity, mean ISI scores and predictive value of individual factors of the ISI score were qualitatively reviewed. FINDINGS: , p<0.0001). The mean ISI score among patients who experienced recurrent instability was also significantly higher than those who did not. CONCLUSIONS AND RELEVANCE: The ISI score as constructed by Balg and Boileau may have clinical utility to help predict recurrent anterior shoulder instability following arthroscopic Bankart repair. However, this review found the threshold values published in their seminal article to be insufficient predictors of recurrent instability. Instead, a lower score threshold may provide as a better predictor of failure. The paucity of level I and II investigations limits the strength of these conclusions, suggesting a need for further large, prospective studies evaluating the predictive ability of the ISI score. LEVEL OF EVIDENCE: IV.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".