Standards and Methodological Rigor in Pulmonary Arterial Hypertension Preclinical and Translational Research
Bibliographic record
Abstract
Despite advances in our understanding of the pathophysiology and the management of pulmonary arterial hypertension (PAH), significant therapeutic gaps remain for this devastating disease. Yet, few innovative therapies beyond the traditional pathways of endothelial dysfunction have reached clinical trial phases in PAH. Although there are inherent limitations of the currently available models of PAH, the leaky pipeline of innovative therapies relates, in part, to flawed preclinical research methodology, including lack of rigour in trial design, incomplete invasive hemodynamic assessment, and lack of careful translational studies that replicate randomized controlled trials in humans with attention to adverse effects and benefits. Rigorous methodology should include the use of prespecified eligibility criteria, sample sizes that permit valid statistical analysis, randomization, blinded assessment of standardized outcomes, and transparent reporting of results. Better design and implementation of preclinical studies can minimize inherent flaws in the models of PAH, reduce the risk of bias, and enhance external validity and our ability to distinguish truly promising therapies form many false-positive or overstated leads. Ideally, preclinical studies should use advanced imaging, study several preclinical pulmonary hypertension models, or correlate rodent and human findings and consider the fate of the right ventricle, which is the major determinant of prognosis in human PAH. Although these principles are widely endorsed, empirical evidence suggests that such rigor is often lacking in pulmonary hypertension preclinical research. The present article discusses the pitfalls in the design of preclinical pulmonary hypertension trials and discusses opportunities to create preclinical trials with improved predictive value in guiding early-phase drug development in patients with PAH, which will need support not only from researchers, peer reviewers, and editors but also from academic institutions, funding agencies, and animal ethics authorities.
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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 | 0.835 | 0.905 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".