Clinical trial design, end-points, and emerging therapies in pulmonary arterial hypertension
Bibliographic record
Abstract
Clinical trials in pulmonary arterial hypertension (PAH) have led to the approval of several effective treatments that improve symptoms, exercise capacity and clinical outcomes. In phase 3 clinical trials, primary end-points must reflect how a patient "feels, functions or survives". In a rare disease like PAH, with an ever-growing number of treatment options and numerous candidate therapies being studied, future clinical trials are now faced with challenges related to sample size requirements, efficiency and demonstration of incremental benefit on traditional end-points in patients receiving background therapy with multiple drugs. Novel clinical trial end-points, innovative trial designs and statistical approaches and new technologies may be potential solutions to tackle the challenges facing future PAH trials, but these must be acceptable to patients and regulatory bodies while preserving methodological rigour. In this World Symposium on Pulmonary Hypertension task force article, we address emerging trial end-points and designs, biomarkers and surrogate end-point validation, the concept of disease modification, challenges and opportunities to address diversity and representativeness, and the use of new technologies such as artificial intelligence in PAH clinical trials.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".