WHO and national lists of essential medicines in Mexico, Central and South America, and the Caribbean: are they adequate to promote paediatric endocrinology and diabetes care?
Bibliographic record
Abstract
Paediatric endocrinology and diabetes is a paediatric specialty with less common conditions and higher cost medicines. Access to medicines for our specialty in low and middle income countries remains limited. We analysed the content of the WHO (children and adults) and of all available national Model Lists of Essential Medicines (EMLs) for Mexico, the Caribbean, Central and South America from a paediatric endocrinology and diabetes standpoint. A master list of medicines deemed necessary in paediatric endocrinology and diabetes was established and compared with the WHO and national EMLs, taking into account the gross national income. The WHO EMLs, which are largely recognised as an international benchmark and drive the content of the national EMLs, included many but not all medicines present on our master list. Interestingly, several national EMLs from richer countries included medicines that were not present in the WHO EMLs. Our analysis suggests that these medicines could be considered by the WHO for inclusion in their EMLs, which may promote the adoption of more medicines by individual countries. We also propose several changes to the WHO and national EMLs that could facilitate access to medicines in our specialty: age cut-off for a child using physical maturity rather than a set age limit; greater standardisation of the formatting of the national EMLs for easier comparison and collaborations between countries; greater emphasis on age-specificity and population-specificity for some medicines; and formatting of the EMLs in a disease-focused manner rather than as individual medicines.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".