StaR Child Health: developing evidence-based guidance for the design, conduct and reporting of paediatric trials
Bibliographic record
Abstract
There has been a huge upsurge in clinical research in children in the last decade, stimulated in England by dedicated research infrastructure and support through the National Institute for Health Research. This infrastructure offering research design, expert review, trial management, research nurse, data support and dedicated facilities enables paediatricians to conduct more and better research. The challenge is how to design and conduct trials that will make a real difference to children's health. Standards for Research (StaR) in Child Health was founded in 2009 to address the paucity and shortcomings of paediatric clinical trials. This global initiative involves methodologists, clinicians, patient advocacy groups and policy makers dedicated to developing practical, evidence-based standards for enhancing the reliability and relevance of paediatric clinical research. In this overview, we highlight the contribution of StaR to this agenda, describe the international context, and suggest how StaR's future plans could be integrated with new and existing support for 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 | 0.392 | 0.513 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.015 | 0.008 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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".