Treatment of Systemic Sclerosis–associated Interstitial Lung Disease: Evidence-based Recommendations. An Official American Thoracic Society Clinical Practice Guideline
Bibliographic record
Abstract
Abstract Background Interstitial lung disease (ILD) is a significant cause of morbidity and mortality in patients with systemic sclerosis (SSc). To date, clinical practice guidelines regarding treatment for patients with SSc-ILD are primarily consensus based. Methods An international expert guideline committee composed of 24 individuals with expertise in rheumatology, SSc, pulmonology, ILD, or methodology, and with personal experience with SSc-ILD, discussed systematic reviews of the published evidence assessed using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Predetermined conflict-of-interest management strategies were applied, and recommendations were made for or against specific treatment interventions exclusively by the nonconflicted panelists. The confidence in effect estimates, importance of outcomes studied, balance of desirable and undesirable consequences of treatment, cost, feasibility, acceptability of the intervention, and implications for health equity were all considered in making the recommendations. This was in accordance with the American Thoracic Society guideline development process, which is in compliance with the Institute of Medicine standards for trustworthy guidelines. Results For treatment of patients with SSc-ILD, the committee: 1) recommends the use of mycophenolate; 2) recommends further research into the safety and efficacy of (a) pirfenidone and (b) the combination of pirfenidone plus mycophenolate; and 3) suggests the use of (a) cyclophosphamide, (b) rituximab, (c) tocilizumab, (d) nintedanib, and (e) the combination of nintedanib plus mycophenolate. Conclusions The recommendations herein provide an evidence-based clinical practice guideline for the treatment of patients with SSc-ILD and are intended to serve as the basis for informed and shared decision making by clinicians and patients.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".