Juvenile Dermatomyositis and Development of Malignancy: 2 Case Reports and a Literature Review
Bibliographic record
Abstract
To the Editor: In adults, there is an established correlation between dermatomyositis (DM) and malignancy1, but in children there are very few case reports in the literature. Here we report 2 cases from our institution of development of leukemia after diagnosis of juvenile DM (JDM). Written consent was provided from the patients (and their parents) included in this report. The Duke University Health System institutional review board (IRB) does not require IRB approval for case reports describing 2 patients. ### Case 1 An obese 6-year-old white girl was admitted with weakness and rash over the preceding 6 months with associated weight loss, fever, and joint pain. Family history was negative for malignancy, immunodeficiency, and autoimmunity. On examination, she had symmetric proximal muscle weakness, heliotrope rash, Gottron papules, nailbed telangiectasias, and no evidence of organomegaly. She had an initial Childhood Myositis Assessment Scale (CMAS) of 14/52. Additionally, she had elevated muscle enzymes, muscle edema on magnetic resonance imaging (MRI), as well as electromyography consistent with myositis. She had a positive antinuclear antibody (ANA), negative … Address correspondence to Dr. L. Cannon, Division of Pediatric Rheumatology, Department of Pediatrics, Duke Children’s Hospital, 2301 Erwin Road, Durham, North Carolina 27710, USA. E-mail: laura.cannon{at}duke.edu
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".