StaR Child Health: developing evidence-based guidance for the design, conduct and reporting of paediatric trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it