Predictors of clinical outcomes after non-operative management of symptomatic full-thickness rotator cuff tears
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that non-surgical management can be an effective treatment strategy for many patients with rotator cuff tears. Despite the prevalence of rotator cuff disease, few studies have examined the patient and tear related factors that predict outcomes of nonsurgical management in this cohort of patients. AIM: To identify factors that are associated with changes in patient reported outcomes over time in individuals with full-thickness rotator cuff tears treated without surgery. METHODS: A cohort of 59 patients who underwent non-surgical management of full thickness rotator cuff tears with a minimum of 1-year follow-up were identified from our institutional registry. Patient demographics, comorbidities and tear characteristics were collected at initial presentation. Outcome measures were collected at baseline and at each clinical follow-up, which included Western Ontario Rotator Cuff (WORC) index, American Shoulder and Elbow Surgeons score, Visual Analog Scale for pain and Single Assessment Numerical Evaluation. Multi- and univariate regression analyses were used to determine the impact of each patient and tear related variable on final WORC scores and change in WORC scores throughout the study. RESULTS: = 0.031). CONCLUSION: Patients with full thickness rotator cuff tears can achieve and maintain clinically meaningful benefit from non-surgical management through 2-year follow-up. Female patients, smokers, and those with significant subscapularis fatty infiltration tend to have lower overall WORC scores at 1-year follow-up, and females also have lower WORC scores at 2-year follow-up. Patients presenting with symptoms greater than 1 year had less clinical improvement at 1-year follow-up, and those with traumatic tears had greater clinical improvement at 2-year follow-up.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".