c4c: Paediatric pharmacovigilance: Methodological considerations in research and development of medicines for children – A c4c expert group white paper
Bibliographic record
Abstract
Children frequently respond differently to therapies compared to adults. Differences also exist between paediatric age groups for pharmacokinetics and pharmacodynamics in both efficacy and safety. Paediatric pharmacovigilance requires an understanding of the unique aspects of children with regard to, for example, drug response, growth and development, clinical presentation of adverse drug reactions (ADRs), how they can be detected and population-specific factors (e.g., more frequent use of off-label/unlicensed drugs). In recognition of these challenges, a group of experts has been formed in the context of the conect4children (c4c) project to support paediatric drug development. This expert group collaborated to develop methodological considerations for paediatric drug safety and pharmacovigilance throughout the life-cycle of medicinal products which are described in this article. These considerations include practical points to consider for the development of the paediatric section of the risk management plan (RMP), safety in paediatric protocol development, safety data collection and analysis. Furthermore, they describe the specific details of post-marketing pharmacovigilance in children using, for example, spontaneous reports, electronic health care records, registries and record-linkage, as well as the use of paediatric pharmacoepidemiology studies for risk characterisation. Next the details of the assessment of benefit-risk and challenges related to medicinal product formulation in the context of a Paediatric Investigation Plan (PIP) are presented. Finally, practical issues in paediatric signal detection and evaluation are included. This paper provides practical points to consider for paediatric pharmacovigilance throughout the life-cycle of medicinal products for RMPs, protocol development, safety data collection and analysis and PIPs.
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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 | 0.383 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".