New perspectives in long-term outcomes in clinical trials of pulmonary arterial hypertension
Bibliographic record
Abstract
The past two decades have seen significant improvements in the management of patients with pulmonary arterial hypertension (PAH). Although outcome has improved, long-term prognosis remains unsatisfactory. The development of new treatment options is clearly important. Equally important is testing new agents in trials designed to provide robust evidence for sustained clinical benefits enabling clinicians to determine the optimal treatment strategy for individual patients. End-points such as the change in 6-min walk distance (6MWD) have been pivotal in the registration trials of currently available PAH-specific therapies. However, as current clinical trials enrol patients with milder disease, many already on background therapy, there is growing evidence that change from baseline in 6MWD is a weak surrogate of outcome in PAH. In addition, while short-term trials allowed for the rapid approval of PAH therapies in the past, there is increasing recognition that clinical trials for new agents must provide evidence of long-term benefits. Clinical trials need to evolve to provide the long-term, clinically relevant data required to appropriately assess new therapies. Event-driven long-term morbidity and mortality trials are currently underway, and will provide robust data on the frequency and timing of events, and are likely to reflect the future of clinical trial design in PAH.
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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 | 0.109 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".