Predictors of Large Labral Tears: A Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Instability Cohort Study
Bibliographic record
Abstract
Objectives: Labral tears are often described by either their location (superior, anterior, or posterior) or their size, commonly defined as degrees of labral involvement from 0° to 360°. Large tears are thought to include 270° or more of the labrum, which has been reported to include 3.3% to 6.5% of those undergoing shoulder instability surgery for labral pathology. Demographic or injury characteristics of those with large labral tears (>270°) has not been defined in the literature. The purpose of this study was 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. Those with an isolated SLAP (superior labrum anterior to posterior) tear or a concomitant rotator cuff tear requiring repair were excluded. Demographic data, injury history, preoperative patient-reported outcome scores (PROs), imaging and intraoperative findings, and surgical procedures performed were recorded. The treating surgeon reported the size and location of labral pathology visualized at the time of surgery. Patients with greater than a 270° tear were defined as having a large labral tear. For categorical demographic variables, a chi-square test or Fisher’s Exact test was used, as appropriate based on cell counts. For continuous demographic variables, a two-sample t-test was performed. In order to build a predictive logistic regression model for large tears, the Feasible Solutions Algorithm (Lambert et al. 2018) was used to add significant interaction effects iteratively until no more significant two-way interactions could be added to the model. 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 old (12 – 66 years old). The incidence of large tears was 4.6% with the average tear size being 141.9°, or 39.4%. Males accounted for significantly more of the large tears seen in the cohort (94.7%, p = 0.01). Racquet sports (p = 0.002), swimming (p = 0.02), softball (p = 0.05), skiing (p = 0.04), and golf (p = 0.04) were all found to be predictive of large labral tears as was a higher Western Ontario Shoulder Instability (WOSI) score (p = 0.01) (Table 1). Patients with a larger body mass index (BMI) who played contact sports were also more likely to have large tears (p = 0.007). Age, race, history of dislocation, injury during sport, or previous shoulder surgery were not associated with having a larger tear. Conclusion: Patients with large labral tears are a small, but not insignificant, subset of patients undergoing shoulder instability surgery. Multiple factors were identified as being associated with large labral tears at the time of surgery including male sex, pre-operative WOSI score, and participation in certain sports including racquet sports, softball, skiing, swimming, and golf. Surgeons treating patients with these risk factors should be prepared to encounter a large labral tear at the time of surgery. Further studies will evaluate the outcomes of this patient population. [Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".