Paediatric oral formulations: Why don't our kids have the medicines they need?
Bibliographic record
Abstract
Medication use in children represents 15-20% of total drug sales. More than 50% of children receive at least one prescription medication a year. Despite this, few drugs have a paediatric formulation available. Furthermore, 80% of paediatric prescriptions are considered off-label. Off-label use is defined as the use of products that differ in dose, indication or route of administration from the one established in the summary of product characteristics. Off-label use is associated with an increased risk of adverse drug reactions, including therapeutic failure. The US Food and Drug Administration and the European Medicines Agency have made changes to regulations to incentivize the development of paediatric formulations. Novel paediatric formulations can ease drug administration, reducing medication errors, increasing dosing acceptability, medication adherence and improve safety. Two routes for paediatric drug approval are available, the traditional, requiring clinical trials and the formulation bridging path, where these formulations need to demonstrate equivalence with the existing adult formulations. New formulations seeking regulatory approval require bioequivalence studies, but the regulatory framework, which states that bioequivalence data are obtained from adults and then extrapolated to children, may be disregarding important physiological differences between these two populations of patients. It is important to ensure that drugs for children have been appropriately studied and are properly manufactured for them. Adequately designed studies will provide data that will improve our understanding of how drug disposition differs between adults and children and will pave the way for children to get the best possible treatment.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".