Mycophenolate Mofetil Improves Lung Function in Connective Tissue Disease-associated Interstitial Lung Disease
Bibliographic record
Abstract
OBJECTIVE: Small series suggest mycophenolate mofetil (MMF) is well tolerated and may be an effective therapy for connective tissue disease-associated interstitial lung disease (CTD-ILD). We examined the tolerability and longitudinal changes in pulmonary physiology in a large and diverse cohort of patients with CTD-ILD treated with MMF. METHODS: We identified consecutive patients evaluated at our center between January 2008 and January 2011 and prescribed MMF for CTD-ILD. We assessed safety and tolerability of MMF and used longitudinal data analyses to examine changes in pulmonary physiology over time, before and after initiation of MMF. RESULTS: We identified 125 subjects treated with MMF for a median 897 days. MMF was discontinued in 13 subjects. MMF was associated with significant improvements in estimated percentage of predicted forced vital capacity (FVC%) from MMF initiation to 52, 104, and 156 weeks (4.9% ± 1.9%, p = 0.01; 6.1% ± 1.8%, p = 0.0008; and 7.3% ± 2.6%, p = 0.004, respectively); and in estimated percentage predicted diffusing capacity (DLCO%) from MMF initiation to 52 and 104 weeks (6.3% ± 2.8%, p = 0.02; 7.1% ± 2.8%, p = 0.01). In the subgroup without usual interstitial pneumonia (UIP)-pattern injury, MMF significantly improved FVC% and DLCO%, and in the subgroup with UIP-pattern injury, MMF was associated with stability in FVC% and DLCO%. CONCLUSION: In a large diverse cohort of CTD-ILD, MMF was well tolerated and had a low rate of discontinuation. Treatment with MMF was associated with either stable or improved pulmonary physiology over a median 2.5 years of followup. MMF appears to be a promising therapy for the spectrum of CTD-ILD.
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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 | 0.000 | 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".