Partial-thickness rotator cuff tears: clinical and imaging outcomes and prognostic factors of successful nonoperative treatment
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the clinical success rate of nonoperative treatment of partial-thickness rotator cuff tears (PT-RCTs), to determine baseline clinical factors predictive of outcome of nonoperative treatment of PT-RCTs, and to determine the imaging outcome of nonoperative treatment of PT-RCTs. PATIENTS AND METHODS: All patients with a primary diagnosis of a PT-RCT were eligible for inclusion. Seventy-six patients (48 males, 28 females) with an average age of 52±10 years were included in the study. Patients were evaluated using a standardized format including clinical, imaging, and shoulder specific quality-of-life outcomes. Patients were assessed and treated either successfully nonoperatively or consented to undergo surgical intervention of their PT-RCT. Patients treated nonoperatively underwent follow-up by MRI arthrogram. RESULTS: Thirty-seven patients (49%) underwent nonoperative treatment. Logistic regression analysis indicated that the baseline variables of side (dominant or nondominant side involved), onset (traumatic or atraumatic), and thickness of tendon tear (<50% or >50%) were significant predictors of outcome. At a mean 46±7 months of follow-up, nonoperatively treated patients demonstrated a mean American Shoulder and Elbow Surgeons score of 85.1±16.0, and a Simple Shoulder Test score of 10.0±2.5. Overall, 76% of tears treated nonoperatively did not show a tear progression on anatomic imaging. Nine patients (24%) demonstrated tear progression, of which three patients (8%) demonstrated full-thickness tearing. CONCLUSION: Nonoperative treatment was utilized in ~50% of the patients and resulted in improved clinical outcomes. Onset, shoulder involved, and thickness of the tear were predictive of the success of nonoperative treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".