Male Sex, Western Ontario Shoulder Instability Index Score, and Sport as Predictors of Large Labral Tears of the Shoulder: A Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Instability Cohort Study
Bibliographic record
Abstract
PURPOSE: To identify factors predictive of a large labral tear at the time of shoulder instability surgery. METHODS: As part of the Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Instability cohort, patients undergoing open or arthroscopic shoulder instability surgery for a labral tear were evaluated. Patients with >270° tears were defined as having large labral tears. To build a predictive logistic regression model for large tears, the Feasible Solutions Algorithm was used to add significant interaction effects. RESULTS: After applying exclusion criteria, 1235 patients were available for analysis. There were 222 females (18.0%) and 1013 males (82.0%) in the cohort, with an average age of 24.7 years (range 12 to 66). The prevalence of large tears was 4.6% (n = 57), with the average tear size being 141.9°. Males accounted for significantly more of the large tears seen in the cohort (94.7%, P = .01). Racquet sports (P = .01), swimming (P = .02), softball (P = .05), skiing (P = .04), and golf (P = .04) were all associated with large labral tears, as was a higher Western Ontario Shoulder Instability Index (WOSI; P = .01). Age, race, history of dislocation, and injury during sport were not associated with having a larger tear. Using our predictive logistic regression model for large tears, patients with a larger body mass index (BMI) who played contact sports were also more likely to have large tears (P = .007). CONCLUSIONS: Multiple factors were identified as being associated with large labral tears at the time of surgery, including male sex, preoperative WOSI score, and participation in certain sports including racquet sports, softball, skiing, swimming, and golf. LEVEL OF EVIDENCE: I, prognostic study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".