The first global landscape analysis of multiple sclerosis research funding
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) is an immune-mediated central nervous system disorder and a growing global health challenge affecting nearly 3 million people worldwide. Incidence and prevalence continue to increase with no known cause or cure. Globally governments and non-profit organizations fund research toward better understanding of and treatments for multiple sclerosis. METHODS: This study identified MS research projects funded between 2021 and 2023 by government and non-profit organization sources. Projects were described by type of scientific approach, Pathways to Cure research category (i.e. Stop, Restore, End), and other key characteristics. RESULTS: Over 2,300 MS research projects were identified through 16 non-profit MS organizations and 18 government databases. The overall global portfolio of these projects is valued at nearly one and a half billion Euros. The majority of projects were classified in the Stop category (60%). Research collaboration occurs in many forms among the research community; around 272 projects were reported to be co-funded. CONCLUSION: Global MS research collaboration will accelerate progress toward increased knowledge, effective treatments, improved health outcomes, and ultimately cures for MS. This landscape analysis highlights the current distribution of MS research investment between topics and begins to suggest where the MS community should focus to increase potential impact for current and future endeavors.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".