Pulmonary Hypertension and Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a disabling disease of the lung parenchyma, characterized by progressive accumulation of scar tissue and myofibroblast activation after repetitive epithelial microinjury. The therapeutic options are limited, and patients usually die within a few years after diagnosis. Pulmonary hypertension (PH) in IPF has been increasingly recognized as a condition with relevance for the overall prognosis. Treatment trials are being designed, but to be effective, it is crucial to better understand the pathobiology of PH in IPF: the traditional concept, that hypoxic vasoconstriction and accumulation of scar tissue are mainly responsible for the development of PH in IPF, has been challenged. Recent studies, including our own in vivo research, suggest that the underlying pathobiology is much more complex, and includes a complicated interaction of epithelial cells, fibroblasts, and vascular cells. This interaction seems to be regulated by a large variety of angiogenesis promoters and inhibitors, as well as growth factors. Central components seem to be endothelial apoptosis and growth factor-induced remodeling of the pulmonary artery wall. The present review gives a conceptual overview about known and putative mechanisms that are involved in the development of PH in IPF. This report summarizes currently available therapeutic options, and also translates experimental research to discuss potential novel biomarkers and therapeutic strategies derived from new concepts in pathogenesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".