Predictors of Pain and Function in Patients With Symptomatic, Atraumatic Full-Thickness Rotator Cuff Tears A Time-Zero Analysis of a Prospective Patient Cohort
Bibliographic record
Abstract
Background: Although the prevalence of full-thickness rotator cuff tears increases with age, many patients are asymptomatic and may not require surgical repair. The factors associated with pain and loss of function in patients with rotator cuff tears are not well defined. Purpose: To determine which factors correlate with pain and loss of function in patients with symptomatic, atraumatic full-thickness rotator cuff tears who are enrolled in a structured physical therapy program. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A multicenter group enrolled patients with symptomatic, atraumatic rotator cuff tears in a prospective, nonrandomized cohort study evaluating the effects of a structured physical therapy program. Time-zero patient data were reviewed to test which factors correlated with Western Ontario Rotator Cuff (WORC) index and American Shoulder and Elbow Surgeons (ASES) scores. Results: A total of 389 patients were enrolled. Mean ASES score was 53.9; mean WORC score was 46.9. The following variables were associated with higher WORC and ASES scores: female sex (P =.001), education level (higher education, higher score; P\\.001), active abduction (degrees; P =.021), and strength in forward elevation (P =.002) and abduction (P =.007). The following variables were associated with lower WORC and ASES scores: male sex (P =.001), atrophy of the supraspinatus (P =.04) and infraspinatus (P =.003), and presence of scapulothoracic dyskinesia (P \\.001). Tear size was not a significant predictor (WORC) unless comparing isolated supraspinatus tears to supraspinatus, infraspinatus, and subscapularis tears (P =.004). Age, tear retraction, duration of symptoms, and humeral head migration were not statistically significant.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".