Bibliographic record
Abstract
Introduction Primary disorders of the pulmonary vasculature are decidedly uncommon. However, secondary involvement of pulmonary blood vessels is very common, being a feature or a complication of many cardiac, pulmonary and other medical conditions. Although we understand more about diseases of the pulmonary blood vessels than just a few decades ago, there is still much less known about the pulmonary vessels than about the systemic blood vessels. However, the presence of similar types of cells in both systemic and pulmonary vessels and a limited range of pathological responses to injury permit students of both to learn from each other. In this chapter, we will review our understanding of the pathogenesis and pathophysiology of pulmonary vascular disease (PVD). PVD may be primary (idiopathic) or secondary to an underlying medical disorder, especially of the heart and lungs. We will focus on one particular entity, primary pulmonary hypertension (PPH) as a prototypical human example of PVD. Although there is much overlap between proposed mechanisms of pulmonary vascular injury in PPH and in secondary PVD, significant clinical and biological heterogeneity exists. Thus, wherever possible, hypothesized mechanisms will be presented with representative, supporting data from studies of patients with PPH. When no such data exist, we will present data from other clinical disorders of PVD, e.g. congenital heart disease (CHD)-associated pulmonary arterial hypertension (PAH), as well as from studies using various animal models of PVD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 0.002 |
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".