A comparison of national essential medicines lists in the Americas
Bibliographic record
Abstract
Objectives. To compare national essential medicines lists (NEMLs) from countries in the Region of the Americas and to identify potential opportunities for improving those lists. Methods. In June of 2017, NEMLs from 31 countries in the Americas were abstracted from documents included in a World Health Organization (WHO) repository. The lists from the Americas were compared to each other and to NEMLs from outside of the Americas, as well as with the WHO Model List of Essential Medicines, 20th edition (“WHO Model List”) and the list of the Pan American Health Organization (PAHO) Regional Revolving Fund for Strategic Public Health Supplies (“Strategic Fund”). Results. The number of differences between the NEMLs from the Americas and the WHO Model List were similar within those countries (median: 295; interquartile range (IQR): 265 to 347). The NEMLs from the Americas were generally similar to each other. While the NEMLs from the Americas coincided well with the Strategic Fund list, some medicines were not included on any of those NEMLs. All the NEMLs in the Americas included some medicines that were withdrawn due to adverse effects by a national regulatory body (median: 8 withdrawn medicines per NEML; IQR: 4 to 12). Conclusions. The NEMLs in the Americas were fairly similar to each other and to the WHO Model List and the Strategic Fund list. However, some areas of treatment and some specific medicines were identified that the countries should reassess when revising their NEMLs.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".