Pulmonary Arterial Hypertension: Challenges in Translational Research and a Vision for Change
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is a vascular remodeling disease with a relentless course toward heart failure and early death. Existing PAH therapies, all of which were developed originally to treat systemic vascular diseases, cannot reverse the disease or markedly improve survival and are expensive. Although there has been a recent increase in the number of potential new therapies emerging from animal studies, less than 3% of the active PAH clinical trials are examining such therapies. There are many potential explanations for the translational gap in this complex multifactorial disease. We discuss these challenges and propose solutions that range from including clinical endpoints in animal studies and improving the rigor of human trials to conducting mechanistic early-phase trials and randomized trials with innovative designs based on personalized medicine principles. Global, independent patient and tissue registries and enhanced communication among academics, industry, and regulatory authorities are needed. The diversity of the mechanisms and pathology of PAH calls for broad comprehensive theories that encompass emerging evidence for contributions of metabolism and inflammation to PAH to support more effective therapeutic target identification.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".