Advances in the treatment of juvenile dermatomyositis
Bibliographic record
Abstract
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".