Recommendations for essential medicines for multiple sclerosis in low-resource settings
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a demyelinating disease of the central nervous system that, when untreated, can lead to significant disability in young adults. Despite the increase in the number of disease-modifying therapies (DMTs), many people living with MS in low-resource settings do not have access to treatment. OBJECTIVE: The primary aim was to develop recommendations on the minimum essential DMTs for MS that should be available in low-resource settings. METHODS: The Multiple Sclerosis International Federation established an independent, international panel including healthcare professionals and people with MS. This panel, in collaboration with the Cochrane MS Group and McMaster GRADE Centre, reviewed evidence for use of MS DMTs following standardized GRADE protocols including consideration of balance of benefits and harms; certainty of evidence; resources required and cost-effectiveness and values, equity, feasibility and availability in low-resource settings. RESULTS: For active and/or worsening forms of relapsing MS, the panel recommends use of ocrelizumab, cladribine, fingolimod, dimethyl fumarate, interferon beta and glatiramer acetate. For active and/or worsening forms of progressive MS, the panel recommends use of rituximab, ocrelizumab, glatiramer acetate, fingolimod and interferon beta. CONCLUSIONS: Recommendations for the minimum essential DMTs for MS in low-resource settings were developed based on robust consideration of evidence and relevant context.
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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 | 0.047 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".