Developments in the Design, Conduct, and Reporting of Child Health Trials
Bibliographic record
Abstract
To identify priority areas to improve the design, conduct, and reporting of pediatric clinical trials, the international expert network, Standards for Research (StaR) in Child Health, was assembled and published the first 6 Standards in Pediatrics in 2012. After a recent review summarizing the 247 publications by StaR Child Health authors that highlight research practices that add value and reduce research "waste," the current review assesses the progress in key child health trial methods areas: consent and recruitment, containing risk of bias, roles of data monitoring committees, appropriate sample size calculations, outcome selection and measurement, and age groups for pediatric trials. Although meaningful change has occurred within the child health research ecosystem, measurable progress is still disappointingly slow. In this context, we identify and review emerging trends that will advance the agenda of increased clinical usefulness of pediatric trials, including patient and public engagement, Bayesian statistical approaches, adaptive designs, and platform trials. We explore how implementation science approaches could be applied to effect measurable improvements in the design, conducted, and reporting of child health research.
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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 | 0.604 | 0.742 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".