Does the Rotator Cuff Tear Pattern Influence Clinical Outcomes After Surgical Repair?
Bibliographic record
Abstract
Background: Limited literature exists regarding the influence of rotator cuff tear morphology on patient outcomes. Purpose: To determine the effect of rotator cuff tear pattern (crescent, U-shape, L-shape) on patient-reported outcomes after rotator cuff repair. Study Design: Cohort study; Level of evidence, 3. Methods: Patients undergoing arthroscopic repair of known full-thickness rotator cuff tears were observed prospectively at regular intervals from baseline to 1 year. The tear pattern was classified at the time of surgery as crescent, U-shaped, or L-shaped. Primary outcome measures were the Western Ontario Rotator Cuff Index (WORC), the American Shoulder and Elbow Surgeons (ASES), and a visual analog scale (VAS) for pain. The tear pattern was evaluated as the primary predictor while controlling for variables known to affect rotator cuff outcomes. Mixed-methods regression and analysis of variance (ANOVA) were used to examine the effects of tear morphology on patient-reported outcomes after surgical repair from baseline to 1 year. Results: A total of 82 patients were included in the study (53 male, 29 female; mean age, 58 years [range, 41-75 years]). A crescent shape was the most common tear pattern (54%), followed by U-shaped (25%) and L-shaped tears (21%). There were no significant differences in outcome scores between the 3 groups at baseline. All 3 groups showed statistically significant improvement from baseline to 1 year, but analysis failed to show any predictive effect in the change in outcome scores from baseline to 1 year for the WORC, ASES, or VAS when tear pattern was the primary predictor. Further ANOVA also failed to show any significant difference in the change in outcome scores from baseline to 1 year for the WORC ( P = .96), ASES ( P = .71), or VAS ( P = .86). Conclusion: Rotator cuff tear pattern is not a predictor of functional outcomes after arthroscopic rotator cuff repair.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".