Skeletal muscle infarction in diabetes mellitus.
Bibliographic record
Abstract
OBJECTIVE: To analyze the risk factors, clinical features, and methods of diagnosis of diabetic muscle infarction (DMI). METHODS: Three patients with diabetes mellitus (DM) and skeletal muscle infarction were studied, and 49 additional cases reported in the English literature (Medline database search) were reviewed. RESULTS: Review of all 52 patients with DMI revealed a number of typical features: equal sex distribution; mean age 41.5 years (range 19-81 yrs); a number of risk factors [long duration of DM (mean 15.2 yrs), poor control and microvascular diabetic complications (neuropathy, retinopathy, nephropathy) (94%), and insulin dependent type I DM (77%)]; a characteristic clinical presentation with painful diffuse muscle swelling (100%); and sometimes a muscle mass (44%), predilection for quadriceps (62%), hip adductors (13%) and leg muscles (13%), elevated serum creatine phosphokinase (47%), abnormal sonograms (81%), abnormal magnetic resonance image (MRI) findings (100%), typical histopathologic findings of a muscle infarct (100%) (ultrastructural evidence of microangiography in one patient); and a tendency toward spontaneous resolution although recurrences are common (51%). CONCLUSION: Skeletal muscle infarction is a rare complication of long standing, poorly controlled DM associated with multiple end organ microvascular sequelae. Increased clinical awareness is important for early recognition, particularly in a diabetic patient presenting with a painful thigh or leg swelling. MR imaging is the diagnostic study of choice, and in the appropriate clinical setting, may obviate the need for a muscle biopsy.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".