Supraspinatus Myotendinous Junction Injuries: MRI Findings and Prevalence
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to describe the MRI findings and evaluate the prevalence of supraspinatus myotendinous injuries. MATERIALS AND METHODS: Among 1001 consecutive shoulders that underwent either conventional MRI or MR arthrography between January and December 2016, 843 shoulders were included. All MR images were retrospectively analyzed for identification and classification into the appropriate grade of acute or chronic rotator cuff myotendinous injuries. Other MRI findings, such as the presence of rotator cuff tendon insertional tears, and clinical information were also evaluated. RESULTS: At MRI, 0.47% (4/843) of shoulders had supraspinatus myotendinous injuries involving the anterior muscular bundle exclusively. Chronic grade III (n = 2), acute grade III (n = 1), and acute grade II (n = 1) injuries were identified in three men and one woman (mean age, 44 years) with a clinical history of trauma (n = 2) or of progressive shoulder pain (n = 2). A concurrent supraspinatus insertional tendon tear with either partial (n = 1) or full (n = 1) thickness was present in half the cases. Loss of tension of the myotendinous junction in grade III myotendinous junction injuries led to severe atrophy and fatty infiltration of the anterior supraspinatus. CONCLUSION: Supraspinatus myotendinous junction injuries are uncommon at MRI. These lesions invariably involve the anterior bundle of the supraspinatus muscle and may occur with a concomitant insertional tendon tear. High-grade chronic injuries lead to selective atrophy and fatty infiltration of the anterior supraspinatus muscle.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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 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".