P77 Pediatric drug data in Canadian drug monographs
Bibliographic record
Abstract
Background Optimal drug therapy in children relies on availability of pediatric-specific information. European and American legislative initiatives have resulted in advancement of pediatric pharmacotherapy data. We aim to describe the quality and quantity of pediatric information in drug monographs of New Active Substances (NASs) approved by Health Canada. Design/Methods Canadian drug monographs of NASs approved by Health Canada, from January 2007 until December 2016, were systematically reviewed for pediatric-specific information. Pediatric-specific information defined as: pediatric indication, dosing, pediatric-friendly dosage forms, and pediatric safety data. Results Over the period of the study, Health Canada approved 281 NASs. Of all the non-biologic NASs (205, 74%), 39(19%) were approved for use in pediatric patients. The number of drugs with pediatric approval was lowest in 2008 (1, 8%) and highest in 2016 (8, 32%), following no specific pattern. Neonates had the lowest rate of drug approvals through all pediatric age groups (4, 2%). All drugs with pediatric approval had pediatric-specific dosing information with the majority of them presenting pediatric safety data (79%). Pediatric friendly formulation was only available in 20%(8) of drugs with pediatric approval. Studies in pediatric populations were the source of pediatric information in 59%(23) of drugs with pediatric approval. Conclusion(s) Less than 20% of the NASs approved by Health Canada for use in adults contain pediatric approval. Neonatal populations remain a therapeutic orphan, with severe lack of dosing and safety information. Safe and effective pediatric pharmacotherapy requires well-conducted pediatric research to enhance pediatric drug data. Canadian children are in need for legislative initiatives to promote pediatric drug development. Disclosure(s) Nothing to disclose
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.030 | 0.050 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".