Magnetic resonance imaging of muscle disease: A pattern‐based approach
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is a powerful tool to assess the severity, distribution, and progression of muscle injury and disease. However, a muscle's response to a pathological insult is limited to only a few patterns on MRI, and findings can be nonspecific. A pattern-based approach is therefore essential to correctly interpret MR studies of abnormal muscle. In this article we review the anatomy, function, and normal MRI appearance of skeletal muscle. We present a classification scheme that categorizes abnormal MR appearances of muscle into 4 main pattern descriptors: (1) distribution; (2) change in size and shape; (3) T1 signal; and (4) T2 signal. Each category is further subdivided into the various patterns seen on MRI. Such an approach allows one to systematically assess abnormal findings on muscle MRI studies and ascertain clues to the diagnosis or differential diagnosis, particularly when findings are correlated with the clinical context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".