The refined Pathways to Cures Research Roadmap for multiple sclerosis cures
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis is a chronic immune-mediated disease of the central nervous system affecting nearly 3 million people worldwide. Although much progress has been made in the understanding and treatment of MS, cures remain elusive. OBJECTIVES: To accelerate the development of cures for MS by updating the Pathways to Cures Research Roadmap based on a contemporary understanding of disease. The refined Roadmap will help to promote research in scientific areas with great potential to reveal insights leading to cures and inspire greater coordination of global resources. METHODS: Refinements to the Roadmap were achieved during a Global Summit that included close to 200 academic and industry scientists, health care providers, policy makers, funders, and people with MS from 15 countries. RESULTS: The refined Roadmap describes three pathways that target opportunities for generating scientific insights leading to cures. Recommendations for accelerating research progress include, lowering barriers for global data sharing, enhancing collaboration and coordination among research supporters, committing to sustained funding, considering implications for implementation, engaging PwMS and committing to diversity, equity, and inclusion in the global MS movement. CONCLUSION: The refined roadmap provides a strategic framework for tackling the complexities of MS and advancing prevention strategies, effective treatments, and cures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".