The Rotator Cuff Quality-of-Life Index Predicts the Outcome of Nonoperative Treatment of Patients with a Chronic Rotator Cuff Tear
Bibliographic record
Abstract
BACKGROUND: Chronic rotator cuff tears are prevalent and can be disabling. The existing literature is unclear regarding the effectiveness of nonoperative treatment. The purposes of this study were to determine whether the outcome of nonoperative treatment can be predicted on the basis of the presenting clinical characteristics and whether the outcome achieved at three months after treatment can be maintained at two years. METHODS: The prospective cohort included ninety-three patients with a documented chronic full-thickness rotator cuff tear. Patients underwent a three-month supervised program of nonoperative treatment and were then evaluated by an orthopaedic surgeon. The treatment outcome was defined as a success if surgical treatment was no longer deemed appropriate by both patient and surgeon because the patient had improved considerably and was predominantly asymptomatic. The outcome was defined as a failure if the patient elected to have surgery after failing to improve and remaining symptomatic. The presenting clinical characteristics that were analyzed included age, sex, smoking status, hand dominance, duration of symptoms, onset (traumatic etiology or insidious onset), shoulder motion, external rotation strength, tear size as documented by ultrasonography or magnetic resonance imaging, and the Rotator Cuff Quality-of-Life Index (RC-QOL). RESULTS: Seventy (75%) of the patients were successfully treated. Logistic regression analysis showed that the baseline RC-QOL score was a significant predictor of outcome (p = 0.017). Eighty-nine percent of patients maintained their three-month outcome at two years of follow-up. CONCLUSIONS: The RC-QOL was predictive of the outcome of nonoperative treatment of patients with a chronic full-thickness rotator cuff tear. Patients in whom the nonoperative treatment was deemed successful at the conclusion of three months of treatment had a very high chance of ongoing success at two years after the initiation of treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".