Pathways to cures for multiple sclerosis: A research roadmap
Bibliographic record
Abstract
BACKGROUND: Multiple Sclerosis (MS) is a growing global health challenge affecting nearly 3 million people. Progress has been made in the understanding and treatment of MS over the last several decades, but cures remain elusive. The National MS Society is focused on achieving cures for MS. OBJECTIVES: Cures for MS will be hastened by having a roadmap that describes knowledge gaps, milestones, and research priorities. In this report, we share the Pathways to Cures Research Roadmap and recommendations for strategies to accelerate the development of MS cures. METHODS: The Roadmap was developed through engagement of scientific thought leaders and people affected by MS from North America and the United Kingdom. It also included the perspectives of over 300 people living with MS and was endorsed by many leading MS organizations. RESULTS: The Roadmap consist of three distinct but overlapping cure pathways: (1) stopping the MS disease process, (2) restoring lost function by reversing damage and symptoms, and (3) ending MS through prevention. Better alignment and focus of global resources on high priority research questions are also recommended. CONCLUSIONS: We hope the Roadmap will inspire greater collaboration and alignment of global resources that accelerate scientific breakthroughs leading to cures for MS.
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 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 | 0.056 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 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".