Tofacitinib in anti-MDA5-positive dermatomyositis-associated interstitial lung disease: a new standard of care emerges
Bibliographic record
Abstract
Extract The management of anti-melanoma differentiation-associated gene 5-positive dermatomyositis with interstitial lung disease (MDA5+DM-ILD) has been a challenge for clinicians worldwide since its recognition in 2005 [1]. Characterised by rapidly progressive interstitial lung disease (RP-ILD), hypomathic myositis, ulcerating Gottron's rashes, and a high 6-month mortality rate, this rare and severe condition often leads to a race against time in the quest for effective treatment [2]. Japanese researchers previously conducted a multicentre prospective study evaluating an intensive initial treatment approach that combined three agents: tacrolimus (a calcineurin inhibitor (CNI)), high-dose glucocorticoids, and intravenous cyclophosphamide [3]. This “triple therapy” showed improved 6-month survival rates in patients with MDA5+DM-ILD. However, a subsequent, larger, real-world study by the same research group found conflicting results: the triple-combination therapy did not demonstrate survival advantages over simpler regimens using glucocorticoids alone or with a single immunosuppressant [4]. Consequently, the current management of this intractable disease remains largely empirical. The rarity and aggressive progression of the disease have posed substantial challenges for conducting clinical trials, leaving the optimal immunosuppressive regimen uncertain.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".