Innovative approaches to investigator-initiated, multicentre paediatric clinical trials in Canada
Bibliographic record
Abstract
Data from clinical trials are needed to guide the safe and effective use of medicines in children. Clinical trials are challenging to design and implement in all populations, and children present additional considerations. Several regions including the UK, USA and Europe have established clinical trial infrastructure to capitalise on expertise and promote clinical trials enrolling children. Our objective is to describe the partnerships and operational considerations for the development of paediatric clinical trials infrastructure in Canada. We describe the design and conduct of four emergency room paediatric trials, with four separate sponsors, across four provinces in parallel. Operations discussed include multisite contract development, centralised risk-based data monitoring, ethical review and patient engagement. We conclude with lessons learnt, additional challenges and potential solutions to facilitate drug development for children in Canada.
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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 | 0.256 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".