Skeletal muscle infarction in diabetes mellitus.
Why this work is in the frame
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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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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) | 0.000 | 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) | 0.001 | 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 it