Limited Predictive Value of the Instability Severity Index Score: Evaluation of 217 Consecutive Cases of Recurrent Anterior Shoulder Instability
Bibliographic record
Abstract
PURPOSE: To review the existing variables and their ability to predict recurrence of shoulder instability as it relates to the Instability Severity Index Score (ISIS), as well as evaluate any other pertinent imaging and patient history variables that may impact risk of recurrent anterior instability after arthroscopic Bankart repair. METHODS: All consecutive patients with recurrent anterior shoulder instability and who had arthroscopic instability repair were identified. Exclusion criteria were prior surgery on the shoulder, posterior or multidirectional instability, instability caused by seizure disorder, or a rotator cuff tear. All ISIS variables were recorded (age <20 years, sport type and level, hyperlaxity, Hill-Sachs on anteroposterior external rotation radiograph, loss of glenoid contour on anteroposterior radiograph), as well as additional variables: (1) number of instability events; (2) total time of instability; (3) glenoid bone loss (GBL) percent; and (4) Hill-Sachs measures (H/L/W/D/Volume). Postoperative outcomes were assessed based on the Western Ontario Shoulder Instability Index (WOSI), Single Assessment Numeric Evaluation (SANE) scores, and American Shoulder and Elbow Surgeons (ASES) scores, and recurrent anterior instability. Regression analysis was used to determine preoperative variables that predicted outcomes and failures. RESULTS: (P = .02), contact sports participation (P = .05), and age 20 years or younger (P < .01). There was no correlation in outcomes with Hill-Sachs on presence of glenoid contour loss on radiograph (P = .07), participation sports, or ISIS (mean = 3.4 success vs 3.9 failure, P > .05). CONCLUSIONS: , and duration of instability symptoms (>3 months). The ISIS may need to be redesigned to incorporate variables that more accurately portray the actual risk of failure after arthroscopic stabilization, including quantification of both glenoid and humeral head bone loss. LEVEL OF EVIDENCE: III (Retrospective Case Series).
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".