Building translational bridges in idiopathic pulmonary fibrosis research: from epithelial dysfunction to dysregulated macrophage polarisation and fibrogenesis
Bibliographic record
Abstract
Extract Idiopathic pulmonary fibrosis (IPF) remains among the most devastating interstitial lung diseases (ILDs), marked by progression, impaired quality of life, poor prognosis and limited therapeutic options. Despite two approved antifibrotic compounds, nintedanib and pirfenidone, and probably a third to come (nerandomilast), IPF patients continue to face unfavourable outcomes, with no available therapy capable of halting or reversing fibrosis [1]. Scientific advancements have deepened our knowledge of many of the underlying pathophysiological mechanisms of IPF in recent years, unveiling molecular intricacies across epithelial, mesenchymal, endothelial and immune compartments. A series of articles published in this issue of the European Respiratory Journal have highlighted many of the key studies discussed here, underscoring the pace of discovery in IPF research. Yet, the translational bridges between discovery and clinical care remain fragile. The field now stands at a key point: whether we can translate scientific insights into real-world clinical implementation (figure 1).
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".