Anifrolumab Therapy in Dermatomyositis: New Insights From Refractory Patients
Bibliographic record
Abstract
OBJECTIVE: To analyze the efficacy and safety of anifrolumab, a monoclonal antibody targeting the type I interferon receptor subunit 1, as a therapeutic option in patients with refractory cutaneous dermatomyositis (DM). METHODS: Patients with DM presenting with cutaneous involvement refractory to different immunosuppressive treatments were enrolled. The Cutaneous Dermatomyositis Disease Area and Severity Index activity (CDASI-A) was used to evaluate the evolution of skin involvement. Manual muscle test-8 (MMT-8) and patient global assessment (PtGA) scores were collected, to evaluate both muscle involvement and patients' quality of life. We registered the changes in glucocorticoid (GC) daily dose, to explore the possible GC-sparing effect of the drug. RESULTS: We enrolled 4 patients (50% female, mean age 60 years). At enrollment, they showed an active cutaneous disease (mean CDASI-A 27/100), mean MMT-8 of 67.5/80, mean daily dose of GC of 10 mg of prednisone, and mean PtGA value of 7.5/10. After treatment, all these variables improved, with mean CDASI-A decreasing to 8.75/100, MMT-8 increasing to 71.3/80, daily GC dose decreasing to 4 mg, and PtGA decreasing to 1.5/10. CONCLUSION: The data we collected showed a significant reduction in CDASI-A values. Further, our results showed an improvement in muscle involvement and in patients' perception of disease burden. Finally, a GC-sparing effect and a good global safety profile of anifrolumab were observed, thus confirming anifrolumab as a new valid therapeutic option in patients with refractory cutaneous DM.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".