Mechanisms of progressive fibrosis in connective tissue disease (CTD)-associated interstitial lung diseases (ILDs)
Bibliographic record
Abstract
Interstitial lung diseases (ILDs), which can arise from a broad spectrum of distinct aetiologies, can manifest as a pulmonary complication of an underlying autoimmune and connective tissue disease (CTD-ILD), such as rheumatoid arthritis-ILD and systemic sclerosis (SSc-ILD). Patients with clinically distinct ILDs, whether CTD-related or not, can exhibit a pattern of common clinical disease behaviour (declining lung function, worsening respiratory symptoms and higher mortality), attributable to progressive fibrosis in the lungs. In recent years, the tyrosine kinase inhibitor nintedanib has demonstrated efficacy and safety in idiopathic pulmonary fibrosis (IPF), SSc-ILD and a broad range of other fibrosing ILDs with a progressive phenotype, including those associated with CTDs. Data from phase II studies also suggest that pirfenidone, which has a different-yet largely unknown-mechanism of action, may also have activity in other fibrosing ILDs with a progressive phenotype, in addition to its known efficacy in IPF. Collectively, these studies add weight to the hypothesis that, irrespective of the original clinical diagnosis of ILD, a progressive fibrosing phenotype may arise from common, underlying pathophysiological mechanisms of fibrosis involving pathways associated with the targets of nintedanib and, potentially, pirfenidone. However, despite the early proof of concept provided by these clinical studies, very little is known about the mechanistic commonalities and differences between ILDs with a progressive phenotype. In this review, we explore the biological and genetic mechanisms that drive fibrosis, and identify the missing evidence needed to provide the rationale for further studies that use the progressive phenotype as a target population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".