Clinical Trials in Neonates and Children: Report of the Pulmonary Hypertension Academic Research Consortium Pediatric Advisory Committee
Bibliographic record
Abstract
Drug trials in neonates and children with pulmonary hypertensive vascular disease pose unique but not insurmountable challenges. Childhood is defined by growth and development. Both may influence disease and outcomes of drug trials. The developing pulmonary vascular bed and airways may be subjected to maldevelopment, maladaptation, growth arrest, or dysregulation that influence the disease phenotype. Drug therapy is influenced by developmental changes in renal and hepatic blood flow, as well as in metabolic systems such as cytochrome P450. Drugs may affect children differently from adults, with different clearance, therapeutic levels and toxicities. Toxicity may not be manifested until the child reaches physical, endocrine and neurodevelopmental maturity. Adverse effects may be revealed in the next generation, should the development of ova or spermatozoa be affected. Consideration of safe, age-appropriate tablets and liquid formulations is an obvious but often neglected prerequisite to any pediatric drug trial. In designing a clinical trial, precise phenotyping and genotyping of disease is required to ensure appropriate and accurate inclusion and exclusion criteria. We need to explore physiologically based pharmacokinetic modeling and simulations together with statistical techniques to reduce sample size requirements. Clinical endpoints such as exercise capacity, using traditional classifications and testing cannot be applied routinely to children. Many lack the necessary neurodevelopmental skills and equipment may not be appropriate for use in children. Selection of endpoints appropriate to encompass the developmental spectrum from neonate to adolescent is particularly challenging. One possible solution is the development of composite outcome scores that include age and a developmentally specific functional classification, growth and development scores, exercise data, biomarkers and hemodynamics with repeated evaluation throughout the period of growth and development. In addition, although potentially costly, we recommend long-term continuation of blinded dose ranging after completion of the short-term, double-blind, placebo-controlled trial for side-effect surveillance, which should include neurodevelopmental and peripubertal monitoring. The search for robust evidence to guide safe therapy of children and neonates with pulmonary hypertensive vascular disease is a crucial and necessary goal.
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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 | 0.207 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.049 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".