Rotator Cuff Tear Morphologic Parameters at Magnetic Resonance Imaging: Relationship With Muscle Atrophy and Fatty Infiltration and Patient-Reported Function and Health-Related Quality of Life
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the relationship between rotator cuff tear (RCT) morphologic parameters and muscle atrophy and fatty infiltration, and patient-reported outcome measures, in patients with symptomatic full-thickness RCT. METHODS: Rotator cuff tear location, length, width, thickness, and musculotendinous junction position were assessed in 57 magnetic resonance imaging scans and correlated to the outcome measures using multivariate regression analysis. RESULTS: Supraspinatus tendon tear length (odds ratio [OR], 2.218; 95% confidence interval [CI], 1.460-3.370), supraspinatus musculotendinous junction position (OR, 2.037; 95% CI, 1.322-3.137), and infraspinatus tendon tear width (OR, 2.371; 95% CI, 1.218-4.615) were identified as the strongest determinants of supraspinatus muscle atrophy, supraspinatus muscle fatty infiltration, and infraspinatus muscle fatty infiltration, respectively. CONCLUSIONS: The extent of supraspinatus tendon and musculotendinous junction retraction influences the development of supraspinatus muscle atrophy and fatty infiltration, whereas the extent of infraspinatus tendon tear width influences the development of infraspinatus muscle fatty infiltration. Morphologic parameters defining RCT at magnetic resonance imaging did not correlate with clinical shoulder function scores.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".