Landscape of MS patient cohorts and registries: Recommendations for maximizing impact
Bibliographic record
Abstract
BACKGROUND: There is a growing number of cohorts and registries collecting phenotypic and genotypic data from groups of multiple sclerosis patients. Improved awareness and better coordination of these efforts is needed. OBJECTIVE: The purpose of this report is to provide a global landscape of the major longitudinal MS patient data collection efforts and share recommendations for increasing their impact. METHODS: A workshop that included over 50 MS research and clinical experts from both academia and industry was convened to evaluate how current and future MS cohorts could be better used to provide answers to urgent questions about progressive MS. RESULTS: The landscape analysis revealed a significant number of largely uncoordinated parallel studies. Strategic oversight and direction is needed to streamline and leverage existing and future efforts. A number of recommendations for enhancing these efforts were developed. CONCLUSIONS: Better coordination, increased leverage of evolving technology, cohort designs that focus on the most important unanswered questions, improved access, and more sustained funding will be needed to close the gaps in our understanding of progressive MS and accelerate the development of effective therapies.
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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 | 0.355 | 0.553 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".