Trends in paediatric clinical pharmacology data in US pharmaceutical labelling
Bibliographic record
Abstract
BACKGROUND: There is often a lack of safety and efficacy data in the paediatric population at the time of drug approval. Legislative efforts have promoted clinical pharmacology research in this underserved population. We sought to determine the quantity and quality of paediatric clinical pharmacology data in US drug labelling at the time of initial approval and to evaluate trends over time. MATERIALS AND METHODS: The labelling data of 213 new molecular entities approved between 2003 and 2012 were systematically reviewed. The type of paediatric pharmacology data present at the time of approval was recorded and stratified by age group. Labelling revisions were analysed for updated paediatric data. The presence of paediatric-specific black-box warnings was noted. RESULTS: Of the 213 drugs evaluated, 48 had adult-specific indications. Of the remaining 165 medicines, only 47 (28%) had paediatric study data at the time of initial labelling. The number of approved drugs with paediatric data was the greatest in 2005 (8, 44%) and was at its lowest point in 2012 (3, 11%). Only five medicines had neonatal data, with none of the anti-infective agents presenting neonatal information. Seven medications had a paediatric-specific black-box warning. Additional 16 medicines presented paediatric data during general labelling updates. CONCLUSIONS: Despite efforts to improve the quality of paediatric clinical pharmacology data, there was not a significant increase in drugs with paediatric data at the time of approval over this 10-year study period. Paediatric drug approvals and labelling revisions continue to lag behind their adult counterparts.
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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 | 0.034 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".