Paediatric oral formulations: Why don't our kids have the medicines they need?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 it