Toward a Treat‐to‐Target Strategy in Juvenile Dermatomyositis: What Are the Suitable Targets and Optimal Timing of Their Achievement?
Bibliographic record
Abstract
OBJECTIVE: Juvenile dermatomyositis (JDM) is a rare autoimmune condition. The treat-to-target strategy has garnered interest in pediatric rheumatology. It is based on defining clear therapeutic targets, with frequent disease activity monitoring, and adjustment of the treatments if targets are not met within a defined time frame. Recently, an international task force of experts launched an initiative aimed at the development of recommendations for the adoption of treat-to-target strategy in JDM. This study was done to support those recommendations. We aimed to determine the time-to-treatment response in patients with JDM, to better inform the development of a treat-to-target strategy in JDM. METHODS: This is a retrospective review of patients with a physician-confirmed diagnosis of JDM, observed at two tertiary care centers-the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Giannina Gaslini (Gaslini), and The Hospital for Sick Children (SickKids). Demographic and clinical data were obtained on all patients with JDM during the first two years following diagnosis. Kaplan-Meier survival curves were used to determine time to outcome definitions. RESULTS: A total of 187 patients were identified across two sites; the mean age of diagnosis was 8 years. On average, patients with JDM achieved normalization of muscle enzymes and muscle remission three months and six months after treatment initiation, respectively. Skin remission occurred within 12 months after starting treatment. Time to reach inactive disease varied between the sites, with median time being 10.3 months (Gaslini) and 8.8 months (SickKids). CONCLUSION: This study provides real-world data for potential timelines to target with a treat-to-target strategy for JDM.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".