Myositis-associated Interstitial Lung Disease: Predictors of Failure of Conventional Treatment and Response to Tacrolimus in a US Cohort
Bibliographic record
Abstract
OBJECTIVE: Patients with myositis-associated interstitial lung disease (MA-ILD) are often refractory to conventional treatment, and predicting their response to therapy is challenging. Recent case reports and small series suggest that tacrolimus may be useful in refractory cases. METHODS: A retrospective cohort study of patients with MA-ILD comparing clinical characteristics between those who responded to or failed conventional treatment. In those who failed conventional treatment and received adjunctive tacrolimus, response to tacrolimus was measured by the improvement in myositis, ILD, and change in the dose of glucocorticoids. RESULTS: Thirty-one of 54 patients (57%) responded to conventional treatment based on the predefined variables of improvement in myositis and/or ILD. Patients with polymyositis (PM)-ILD were more likely to respond to conventional treatment than those with dermatomyositis (DM)-ILD (67% vs 35%, p = 0.013). Twenty-three patients failed conventional treatment, 18 of whom subsequently received adjunctive tacrolimus. Ninety-four percent had improvements in ILD and 72% showed improvement in both myositis and ILD. The mean doses of prednisone decreased from baseline by 65% at 3-6 months (p = 0.002) and 81% at 1 year (p < 0.001). CONCLUSION: Patients with PM-ILD were more likely to respond to conventional treatment than patients with DM-ILD, but clinical characteristics and serology did not otherwise predict response to therapy. A majority of patients with MA-ILD refractory to conventional therapy improved while receiving tacrolimus and were able to decrease their dose of both glucocorticoids and other disease-modifying antirheumatic drugs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".