Do the current MS clinical course descriptors need to change and if so how? A survey of the MS community
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The current clinical course descriptors of multiple sclerosis (MS) include a combination of clinical and magnetic resonance imaging (MRI) features. Recently there has been a growing call to base these descriptors more firmly on biological mechanisms. We investigated the implications of proposing a new mechanism-driven framework for describing MS. METHODS: In a web-based survey, multiple stakeholders rated the need to change current MS clinical course descriptors, the definitions of disease course and their value in clinical practice and related topics. RESULTS: We received 502 responses across 49 countries. In all, 77% of the survey respondents supported changing the current MS clinical course descriptors. They preferred a framework that informs treatment decisions, aids the design and conduct of clinical trials, allows patients to understand their disease, and links disease mechanisms and clinical expression of disease. Clinical validation before dissemination and ease of communication to patients were rated as the most important aspects to consider when developing any new framework for describing MS. CONCLUSION: A majority of MS stakeholders agreed that the current MS clinical course descriptors need to change. Any change process will need to engage a wide range of affected stakeholders and be guided by foundational principles.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".