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Record W2999159177 · doi:10.3899/jrheum.190929

Juvenile Dermatomyositis and Development of Malignancy: 2 Case Reports and a Literature Review

2020· review· en· W2999159177 on OpenAlexvenueno aff
Laura Cannon, Jeffrey Dvergsten, Cory Stingl

Bibliographic record

VenueThe Journal of Rheumatology · 2020
Typereview
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineJuvenile dermatomyositisDermatomyositisAnti-nuclear antibodyRashMalignancyDermatologyMyositisPediatricsOrganomegalyMuscle weaknessSurgeryInternal medicinePolyneuropathyImmunologyAutoantibody

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.531
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.291
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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