Pediatric formulations in national essential medicines lists: a cross-sectional study
Bibliographic record
Abstract
Background: Children need specialized medicine formulations for proper dosing, safety, and adherence. The World Health Organization developed a pediatric essential medicines list prioritising pediatric formulations. Given global commitment by countries to children's health, we expected national lists similarly prioritizing pediatric medicines and formulations. We assessed the extent to which national lists include medicines for children. Methods: to present granular data. Data were categorized by country characteristics and income levels. Results: Our study found that most countries do not include pediatric formulations in their Essential Medicine Lists (EMLs), especially high-income European countries. Of the 22 countries that do, most list medicines for infections, antiretrovirals, and cancer, but gaps exist for antitrypanosomal, antileishmanial, and antihepatitis treatments. Paracetamol had the most diverse formulations. Additionally, differences were found between national and World Health Organization (WHO) EMLs, with some countries listing fewer medicines overall, though some countries included more treatments for HIV and hepatitis than the WHO Essential Medicines List for children (EMLc). Conclusion: In many countries, it is unclear which medicines for children are prioritized, if any. The problem is particularly acute in high-income countries. Misalignments between national lists and the World Health Organization are common. There is little evidence that countries are adequately implementing medicines policies for youth.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".