Progressive MS: from pathophysiology to drug discovery
Bibliographic record
Abstract
Progressive multiple sclerosis (MS) will be a major area of research interest for years to come. No treatments exist and success in the field will generalise to other neurological conditions where neurodegeneration coexists with neuroinflammation. The issue is complex, and interdisciplinary approaches - uniting scientists with different competences (neurobiology, immunogenetics, etc.) and 'mindsets' (academia and industry) - will be decisive. The International Progressive MS Alliance is catalysing this process through various initiatives, the most recent of which was a meeting where scientists from academia (also outside the MS field) and from industry reviewed data and strategies to determine the next steps towards the translation of current knowledge into effective therapies.Key findings are:(i). Concerted efforts are essential to prioritise pathogenetic mechanisms according to impact on the disease and druggability.(ii). Combination therapies will probably be needed, possibly early in the disease, along with new trial designs and treatment schedules.(iii). Drug screenings are a pragmatic approach hopefully enriched by the use of neural and oligodendrocyte progenitors differentiated from induced pluripotent stem cells (iPSCs).(iv). The field of network biology will increase our ability to predict therapeutic targets.(v). Genome-wide association studies (GWAS) must try to identify variants associated with disease progression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".