Association between immunosuppressive therapy and course of mild interstitial lung disease in systemic sclerosis
Bibliographic record
Abstract
OBJECTIVE: Interstitial lung disease (ILD) is a leading cause of mortality in SSc. Little is known about the benefits of immunosuppressive drugs in mild ILD. Our aim was to determine whether use of CYC or MMF was associated with an improved ILD course in patients with normal or mildly impaired lung function. METHODS: A retrospective cohort of SSc subjects with ILD, disease duration below seven years and no exposure to CYC or MMF prior to the baseline visit was constructed from the Canadian Scleroderma Research Group registry. Subjects were categorized as having mild ILD if baseline forced vital capacity (FVC % predicted) was >85%. The primary exposure was any use of CYC or MMF at the baseline visit. FVC at one year was compared between exposed and unexposed subjects, using multivariate linear regression. RESULTS: Out of 294 eligible SSc-ILD subjects, 116 met criteria for mild ILD. In this subgroup, mean (s.d.) disease duration was 3.7 (2.0) years. Thirteen (11.2%) subjects were exposed to CYC or MMF at baseline. The one-year FVC was higher in exposed subjects compared with unexposed subjects, by a difference of 8.49% (95% CI: 0.01-16.98%). None of the exposed subjects experienced clinically meaningful progression over two years, whereas 24.6% of unexposed subjects did. CONCLUSION: In this real-world setting, CYC/MMF exposure at baseline was associated with higher FVC values and a lower risk of progression among subjects with mild ILD. These data suggest a window of opportunity to preserve lung function in SSc-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 | 0.001 | 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) | 0.002 | 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".