Lipodystrophy in patients with juvenile dermatomyositis--evaluation of clinical and metabolic abnormalities.
Bibliographic record
Abstract
OBJECTIVE: Lipodystrophy and associated metabolic abnormalities are being increasingly recognized as complications of juvenile dermatomyositis (JDM). We investigated the prevalence of lipodystrophy and the extent of metabolic abnormalities related to lipoatrophic diabetes mellitus in patients with JDM. METHODS: Twenty patients with JDM were evaluated for evidence of lipodystrophy and associated lipoatrophic diabetes mellitus. All patients underwent clinical assessment, laboratory investigations, and metabolic studies (oral glucose tolerance test, lipid studies, insulin antibodies). RESULTS: We found clinical evidence of lipodystrophy and lipoatrophic diabetes mellitus in 4 of 20 patients with JDM and metabolic abnormalities known to be associated with lipodystrophy in another 8 patients. The 20 patients with JDM were categorized as follows: Group 1 (Patients 1-4) consisted of patients with lipodystrophy and either diabetes mellitus (2 patients) or impaired glucose tolerance (2 patients); Group 2 (Patients 5-12): no lipodystrophy but abnormal glucose and/or lipid studies; Group 3 (Patients 13-20): no lipodystrophy and no abnormalities of glucose and lipid studies. CONCLUSION: We found 25% of patients with JDM have lipodystrophy, and 50% present with hypertriglyceridemia and insulin resistance. Screening for metabolic abnormalities in JDM should be included in routine followup because of the effect of lipodystrophy on longterm prognosis.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".