Incidence and progression of interstitial lung disease in systemic sclerosis patients without significant interstitial lung disease: associations with immunosuppressive drugs
Bibliographic record
Abstract
Background: Interstitial lung disease (ILD) is a leading cause of mortality in systemic sclerosis (SSc). Immunosuppressive drugs are currently used to treat ILD, but are generally ineffective for reversing established damage. A retrospective cohort study design was used to explore the effect of immunosuppressive drugs in milder forms of ILD, as well as to study the association between use of immunosuppressive drugs and risk of incident ILD, using data from patients enrolled in the Canadian Scleroderma Research Group registry between 2004 and 2017.Methods: First, SSc patients with mild ILD (i.e. baseline forced vital capacity (FVC) above 85%) (N=116) were compared according to their exposure to cyclophosphamide (CYC) and/or mycophenolate mofetil (MMF). FVC was assessed at one year using a multivariate linear regression model. Then, SSc patients without lung disease at baseline (N=1,131) were compared according to their exposure to CYC, MMF, azathioprine and/or methotrexate. Time to incident ILD was assessed using a marginal structural Cox model incorporating inverse probability of treatment weights.Results: Among SSc patients with mild ILD, the one-year FVC was higher in patients exposed to CYC/MMF at baseline compared to unexposed patients, by a difference in predicted FVC of 8.49% (95% CI, 0.01 to 16.98). Baseline FVC was identified as an effect modifier on the relationship between CYC/MMF exposure and lung disease progression. However, among SSc patients without lung disease at baseline, exposure to immunosuppressive drugs did not significantly reduce the risk of incident ILD (weighted HR: 0.78, 95% CI 0.44 to 1.37).Conclusions: These findings lend support to the hypothesis that a therapeutic window of opportunity may exist in SSc-ILD. Treatment at a stage when lung function is still normal should be considered as a means of preventing progressive disease before significant functional compromise has occurred.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".