A Retrospective Analysis of Outcome in Melanoma Differentiation–Associated Gene 5–Related Interstitial Lung Disease Treated with Tofacitinib or Tacrolimus
Bibliographic record
Abstract
Objective The efficacy of tofacitinib (TOF) in the early diagnosis of melanoma differentiation–associated gene 5 (MDA5)–related interstitial lung disease (ILD) has been described. However, whether TOF exposure is associated with a reduced 1-year mortality rate remains undetermined. Methods Patients diagnosed with MDA5-ILD receiving TOF or tacrolimus (TAC) treatment were included. A Cox proportional hazards model, which was adjusted for age, sex, smoking history, anti-MDA5 antibody titers, and concurrent use of other steroid-sparing agents, was performed to compare all-cause mortality and to investigate the risk factors predicting 1-year mortality rates in the 2 treatment groups. Results During the study period, 26 patients were treated with TOF and 35 were treated with TAC. The 6-month (38.5% vs 62.9%;P= 0.03) and 1-year (44.0% vs 65.7%;P= 0.03) mortality rates in the TOF group were significantly lower than those in the TAC group. There were 13 patients diagnosed with rapidly progressive ILD (RP-ILD) in the TOF group and 22 in the TAC group. The majority of deaths occurred in patients with RP-ILD. The 6-month (76.9% vs 95.5%;P= 0.02) and 1-year (84.6% vs 100.0%;P= 0.02) mortality rates of patients with RP-ILD in the TOF group were also lower than those in the TAC group, respectively. The adjusted model showed that TOF exposure was associated with a lower risk for 1-year mortality (hazard ratio 0.44, 95% CI 0.20-0.96;P= 0.04). However, the incidence of adverse events (73.1% vs 74.3%;P> 0.99) and medication discontinuation rates (23.1% vs 14.3%;P= 0.50) in the TOF and TAC groups were similar, respectively. Conclusion Our observational study showed that TOF use might have a potential effect on improving the outcomes of MDA5-ILD. Future clinical trials are needed to assess the long-term efficacy and tolerability of TOF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".