Prioritizing progressive MS rehabilitation research: A call from the International Progressive MS Alliance
Bibliographic record
Abstract
BACKGROUND: People with multiple sclerosis (MS) experience myriad symptoms that negatively affect their quality of life. Despite significant progress in rehabilitation strategies for people living with relapsing-remitting MS (RRMS), the development of similar strategies for people with progressive MS has received little attention. OBJECTIVE: To highlight key symptoms of importance to people with progressive MS and stimulate the design and implementation of high-quality studies focused on symptom management and rehabilitation. METHODS: A group of international research experts, representatives from industry, and people affected by progressive MS was convened by the International Progressive MS Alliance to devise research priorities for addressing symptoms in progressive MS. RESULTS: Based on information from the MS community, we outline a rationale for highlighting four symptoms of particular interest: fatigue, mobility and upper extremity impairment, pain, and cognitive impairment. Factors such as depression, resilience, comorbidities, and psychosocial support are described, as they affect treatment efficacy. CONCLUSIONS: This coordinated call to action-to the research community to prioritize investigation of effective symptom management strategies, and to funders to support them-is an important step in addressing gaps in rehabilitation research for people affected by progressive 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 | 0.376 | 0.237 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.030 | 0.032 |
| Open science | 0.010 | 0.043 |
| Research integrity | 0.050 | 0.078 |
| Insufficient payload (model declined to judge) | 0.010 | 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".