A 5-Year Follow-up of Patients Treated for Full-Thickness Rotator Cuff Tears: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The evidence in support of operative versus nonoperative management of rotator cuff tears (RCTs) is limited, based primarily on observational studies of lower scientific merit. PURPOSE: To (1) compare the efficacy of operative versus nonoperative management of full-thickness RCTs across time and (2) detect variables that predict success within each group. STUDY DESIGN: Cohort study; Level of evidence, 2. METHODS: We included patients with symptomatic full-thickness RCTs who were enrolled in an institutional shoulder registry. Patient enrollment began in 2009 and continued until early 2018. The following outcome measures were collected at baseline, then 6 months, 1 year, and annually up to 5 years postoperatively: Western Ontario Rotator Cuff Index (WORC), American Shoulder and Elbow Surgeons (ASES) score, Veterans RAND 12-Item Health Survey (VR-12) mental and physical component subscales (MCS and PCS, respectively), 100-point Single Assessment Numeric Evaluation (SANE) rating, and 100-point visual analog scale (VAS) for pain and for patient satisfaction. We performed regression models for all outcome variables across all 5 years of follow-up and included the following predictor variables: treatment type (operative vs nonoperative), sex, age, symptom duration, smoking status, diabetes status, injury side, and obesity status. RESULTS: A total of 595 patients were included. Longitudinal mixed-effects regression revealed that patients who received operative treatment did better across time on all outcomes. Women (n = 242; 40.7%) did not fare as well as did men on the ASES, WORC, or VR-12 PCS. Older patients tended to improve less on the VR-12 PCS and more on the VR12-MCS. Patients with longer symptom duration at baseline had better scores across time on the ASES, WORC, VAS for pain, and SANE. Current or recent smokers and patients with diabetes tended to have lower scores on all measures across time. For changes in scores from baseline, patients in the operative group improved to a larger degree out to 3 years compared with those in the nonoperative group. CONCLUSION: Patients with RCTs tended to improve regardless of whether they received operative or nonoperative treatment, but patients who underwent operative treatment improved faster. There appear to be several predictors of improved and worsened outcomes for patients with RCTs undergoing operative or nonoperative 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".