Current and Future Treatment Landscape for Idiopathic Pulmonary Fibrosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) remains a disease with poor survival. The pathogenesis is complex and encompasses multiple molecular pathways. The first-generation antifibrotics pirfenidone and nintedanib, approved more than 10 years ago, have been shown to reduce the rate of progression, increase the length of life for patients with IPF, and work for other fibrotic lung diseases. In the last two decades, most clinical trials on IPF have failed to meet the primary endpoint and an urgent unmet need remains to identify agents or treatment strategies that can stop disease progression. The pharmacotherapeutic landscape for IPF is moving forward with a number of new drugs currently in clinical development, mostly in phase I and II trials, while only a few phase III trials are running. Since our understanding of IPF pathogenesis is still limited, we should keep focusing our efforts to deeper understand the mechanisms underlying this complex disease and their reflection on clinical phenotypes. This review discusses the key pathogenetic concepts for the development of new antifibrotic agents, presents the newest data on approved therapies, and summarizes new compounds currently in clinical development. Finally, future directions in antifibrotics development are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it