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Advances in the treatment of juvenile dermatomyositis

2006· article· en· W1968798526 on OpenAlexaff
ELIZABETH A. STRINGER, Brian M. Feldman

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2006
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsJuvenile dermatomyositisMedicineDermatomyositisRituximabRashIntensive care medicineDiseaseMethotrexateRefractory (planetary science)JuvenileDermatologyPediatricsPhysical therapyInternal medicineLymphoma

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Juvenile dermatomyositis is a rare chronic inflammatory disease that primarily affects the muscles and skin. Immunosuppressive therapy has played a very important role in reducing mortality rates and morbidity. The review focuses on the spectrum of medications currently used in the treatment of juvenile dermatomyositis, highlighting new advances and unanswered questions. RECENT FINDINGS: Data regarding the treatment of juvenile dermatomyositis come almost entirely from retrospective studies with relatively small numbers of patients. Corticosteroids continue to be the accepted first-line therapy. Evidence that the addition of methotrexate at initiation of treatment allows corticosteroids to be tapered more rapidly with good outcomes exists. High-risk, refractory patients may benefit from intravenous cyclophosphamide. Results in refractory patients treated with rituximab are also encouraging. Topical immunosuppressant agents have been largely disappointing in treating rash. The effect and role of exercise in the treatment and rehabilitation of patients with juvenile dermatomyositis is an interesting new area of research. SUMMARY: Future research in the treatment of juvenile dermatomyositis should focus on improving the understanding of disease course and its predictors such that treatment protocols can be developed to provide the most benefit and least amount of medication toxicity for the individual patient.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.321
Teacher spread0.302 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations56
Published2006
Admission routes1
Has abstractyes

Explore more

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