Prognostic factors for worsening and improvement in multiple sclerosis using a multistate model
Bibliographic record
Abstract
BACKGROUND: The long-term disease trajectory of people living with multiple sclerosis (MS) can be improved by initiating efficacious treatment early. More quantitative evidence is needed on factors that affect a patient's risk of disability worsening or possibility of improvement to inform timely treatment decisions. METHODS: We developed a multistate model to quantify the influence of demographic, clinical, and imaging factors on disability worsening and disability improvement simultaneously across the disability spectrum as measured by the Expanded Disability Status Scale (EDSS). We used clinical trial data from the Novartis-Oxford MS database including ~130,000 EDSS assessments from ~8000 patients, spanning all MS phenotypes. RESULTS: Higher brain volume was positively associated with disability improvement at all disability levels (hazard ratio (HR) = 1.09-1.19; 95% credible interval (CI) = 1.02-1.27). Higher T2 lesion volume was negatively associated with disability improvement up to EDSS 6 (HR = 0.80-0.89; 95% CI = 0.75-0.94). Older age, time since first symptoms, and the number of relapses in the past year were confirmed as predictors of future disability worsening. CONCLUSIONS: Brain damage was identified as the most consistent factor limiting the patient's probability for improvements from the earliest stages and across the whole course of MS. Protecting brain integrity early in MS should have greater weight in clinical decision-making.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".