The Cartilage Wear Index: a new evaluation method to improve patient selection in surgical treatment of recurrent posterior glenohumeral instability
Bibliographic record
Abstract
Background The purpose of this study was to validate glenoid cartilage lesions as a negative prognostic factor and to define a new image-based preoperative evaluation method to identify surgical candidates for arthroscopic labral refixation with suture anchors in posterior shoulder instability. Methods Twenty-six patients who underwent arthroscopic posterior labral repair for shoulder instability were evaluated. Only patients with structural dynamic posterior instability were included. We evaluated on preoperative magnetic resonance arthrogram: glenoid version, humeral head subluxation, type of capsular insertion, and the cartilage lesions using the new Cartilage Wear Index (CWI). Two subgroups were analyzed with regard to the preoperative CWI and shoulder outcome scores: Single Assessment Numerical Evaluation (SANE) and Western Ontario Shoulder Instability Index (WOSI). Results The median age at operation was 28 (interquartile range = 21-33) years. Median overall postoperative outcome assessment demonstrated a SANE of 90 and a WOSI of 385. The median CWI was 1.02. Subgroup analysis revealed worse median WOSI and SANE scores in patients with a CWI >1.02 and a strong correlation between a high preoperative CWI and a higher postoperative WOSI score (R = 0.58; P = .038). Conclusion The CWI can be useful to identify patients who might obtain better outcomes when treated with arthroscopic labral repair in posterior shoulder instability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".