New treatment and markers of prognosis for idiopathic pulmonary fibrosis: lessons learned from translational research
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease with increasing prevalence, high mortality rates and poor treatment options. The diagnostic process is complex and often requires an interdisciplinary approach between different specialists. Information gained over the past 10 years of intense research resulted in improved diagnostic algorithms, a better understanding of the underlying pathogenesis and the development of new therapeutic options. Specifically, the change from the traditional concept that viewed IPF as a chronic inflammatory disorder to the current belief that is primarily resulting from aberrant wound healing enabled the identification of novel treatment targets. This increased the clinical trial activity dramatically and resulted in the approval of the first IPF-specific therapy in many countries. Still, the natural history and intrinsic behavior of IPF are very difficult to predict. There is an urgent need for new therapies and also for development and validation of prognostic markers that predict disease progression, survival and also response to antifibrotic drugs. This review provides an up to date summary of the most relevant clinical trials, novel therapeutic drug targets and outlines a spectrum of potential prognostic biomarkers for IPF.
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.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".