Surgical Versus Nonsurgical Management of Rotator Cuff Tears
Bibliographic record
Abstract
Background: Rotator cuff tears are a common shoulder disorder resulting in significant disability to patients and financial burden on the health care system. While both surgical and nonsurgical management are accepted treatment options, there is a paucity of data to support a treatment algorithm for care providers. Defining variables to guide treatment allocation may be important for patient education and counseling, as well as to deliver the most efficient care plan at the time of presentation. Purpose: To identify independent variables at the time of initial clinical presentation that are associated with preferred allocation to surgical versus nonsurgical management for patients with known full-thickness rotator cuff tears. Study Design: Case control study; Level of evidence, 3. Methods: A total of 196 consecutive adult patients with known full-thickness rotator cuff tears were enrolled into a prospective cohort study. Robust data were collected for each subject at baseline, including age, sex, body mass index (BMI), shoulder activity score, smoking status, size of cuff tear, duration of symptoms, functional comorbidity index, the American Shoulder and Elbow Surgeons (ASES) score, the Western Ontario Rotator Cuff index (WORC), and the Veterans Rand 12-Item Health Survey (VR-12). Logistic regression was performed to identify variables associated with treatment allocation, and the corresponding odds ratios were calculated. Results: Of the 196 patients enrolled, 112 underwent surgical intervention and 84 nonoperative management. With covariates controlled for, significant baseline patient characteristics predictive of eventual allocation to surgical treatment included younger age, lower BMI, and durations of symptoms less than 1 year. Increasing age, higher BMI, and duration of symptoms longer than 1 year were predictive of nonsurgical treatment. Factors that were not associated with treatment allocation included sex, tear size, functional comorbidity score, or any of the patient-derived outcome scores at presentation (ASES, WORC, VR-12, shoulder activity score). Conclusion: Patient demographics at the time of initial presentation for a symptomatic rotator cuff tear are more predictive of treatment allocation to a surgical or nonoperative approach than the patient-derived outcome scores for activity level and shoulder disability. Further study is warranted to help define appropriate indications for treatment allocation in patients with rotator cuff tears.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".