2023 American College of Rheumatology (ACR)/American College of Chest Physicians (CHEST) Guideline for the Treatment of Interstitial Lung Disease in People with Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: We provide evidence-based recommendations regarding the treatment of interstitial lung disease (ILD) in adults with systemic autoimmune rheumatic diseases (SARDs). METHODS: We developed clinically relevant population, intervention, comparator, and outcomes questions. A systematic literature review was then performed, and the available evidence was rated using the Grading of Recommendations, Assessment, Development, and Evaluation methodology. A panel of clinicians and patients reached consensus on the direction and strength of the recommendations. RESULTS: Thirty-five recommendations were generated (including two strong recommendations) for first-line SARD-ILD treatment, treatment of SARD-ILD progression despite first-line ILD therapy, and treatment of rapidly progressive ILD. The strong recommendations were against using glucocorticoids in systemic sclerosis-ILD as a first-line ILD therapy and after ILD progression. Otherwise, glucocorticoids are conditionally recommended for first-line ILD treatment in all other SARDs. CONCLUSION: This clinical practice guideline presents the first recommendations endorsed by the American College of Rheumatology and American College of Chest Physicians for the treatment of ILD in people with SARDs.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".