World Health Organization Essential Medicines List: Multiple sclerosis disease-modifying therapies application
Bibliographic record
Abstract
INTRODUCTION: The World Health Organization (WHO) publishes a biennial Essential Medicines List (EML) to assist governments in low-resource settings to prioritize their spending on medicines. Currently, no medicines on the EML have a multiple sclerosis (MS) indication. Multiple Sclerosis International Federation (MSIF) prepared an application for inclusion of MS disease-modifying therapies (DMTs) for the 2019 EML together with the regional Committees for Treatment and Research in Multiple Sclerosis (TRIMS) and the World Federation of Neurology. RATIONALE: The MSIF taskforce categorized 15 DMTs according to their efficacy and risk profiles to ensure the ability to treat as many different clinical scenarios as possible. Three DMTs were selected: glatiramer acetate, fingolimod, and ocrelizumab. OUTCOME: The WHO Expert Committee did not recommend the addition of any of the DMTs to the EML. They acknowledged the public health burden of MS, the need for effective and affordable MS medications, and the high volume of letters received in support of the application but requested a revised application. DISCUSSION: Despite the negative outcome, the repeated recognition of MS as a global public health burden is sending a powerful message to governments globally that a range of affordable and good quality medications need to be available to health systems and people affected by MS.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.060 | 0.042 |
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".