Association between pre‐operative magnetic resonance imaging and reparability of large and massive rotator cuff tears
Bibliographic record
Abstract
PURPOSE: It is recognized that a percentage of large and massive rotator cuff tears (RCTs) cannot be anatomically repaired. We hypothesized that factors identified on pre-operative MRI would be associated with rotator cuff reparability. METHODS: A single-surgeon retrospective study was performed on patients who had undergone either an anatomical or partial repair of a large or massive RCT. Pre-operative MRI images were evaluated by a fellowship-trained shoulder surgeon, blinded to the surgical outcome. Stump location, tear dimension in the coronal and sagittal plane, fatty infiltration, muscle atrophy (occupation ratio, tangent sign), and superior migration of the humeral head (acromion-humeral distance, inferior glenohumeral distance, and best-fit humeral circle technique) were assessed as the predictors of repair. Logistic regression and chi-square analyses were used. RESULTS: A total of sixty patients with median age 63 (range 40-83) were included. On MRI, reparability was associated with increased medial-lateral (ML) tear size (p = 0.003), but not increased AP tear size (n.s.). An association was seen between partial repair and tendon retraction to or beyond the glenoid (p = 0.0005), positive tangent sign (p = 0.04), advanced supraspinatus fatty infiltration in isolation (p = 0.046), combined advanced supraspinatus and infraspinatus fatty infiltration (p = 0.04), and superior migration of the humeral head as measured by the inferior glenohumeral distance only (p = 0.004). Multivariable analysis identified increased ML tear size as the most significant factor associated with partial repair. CONCLUSION: This study demonstrates that MRI findings of tendon retraction to or beyond the glenoid, increased inferior glenohumeral distance, and a positive tangent sign are associated with irreparability of large and massive RCTs.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".