247 Clinically silent MRI lesions in RRMS: prognostic impact and an emulated trial of disease modifying therapy escalation
Bibliographic record
Abstract
a:2:{s:4:"lang";s:2:"en";s:7:"content";s:1770:" Background Clinical guidelines for relapsing remitting multiple sclerosis (RRMS) do not recommend disease modifying therapy (DMT) escalation after a single on-treatment clinically silent MRI lesion (CSL). Methods From the MSBase international registry, we studied adults with RRMS who had MRI brain scans 6-18 months apart while clinically stable and established on a DMT. Cox models compared outcomes (relapse and 6 month confirmed disability worsening [CDW]) in those with and without CSLs, adjusted for sex, age, calendar year, MS duration, EDSS score, relapses in the prior 2 years, DMT efficacy and country health expenditure. In those with CSLs on platform or moderate-efficacy DMTs, an emulated a target trial compared DMT escalation within 6 months of a silent lesion versus unchanged DMT (unless a post-MRI clinical event occurred). Results Among 10,308 people with RRMS, 2-year hazard ratios (HR [95% confidence interval]) comparing CSL with no CSL were 1.78 [1.59–2.00] for relapse and 1.38 [1.18–1.61] for CDW. Associations were similar in people with both single and multiple CSLs, on platform and moderate-efficacy baseline DMTs, and regardless of disease duration. In 2,295 people with CSLs on platform and moderate-efficacy DMTs, DMT escalation (n=288) reduced the median 4-year relapse risk from 38.9% to 17.0% (HR 0.34 [0.23–0.48]). Conclusions People with RRMS with single or multiple on-treatment clinically silent MRI lesions have higher subsequent risks of relapse and disability worsening than those without CSLs. DMT escalation mitigates the relapse risk. Contrary to clinical guidelines, DMT escalation should be considered even after a single CSL. cyrus.daruwalla{at}nhs.net ";}
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".