Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the extent to which treatment effect on brain atrophy is able to mediate, at the trial level, the treatment effect on disability progression in relapsing-remitting multiple sclerosis (RRMS). METHODS: We collected all published randomized clinical trials in RRMS lasting at least 2 years and including as endpoints disability progression (defined as 6 or 3 months confirmed 1-point increase on the Expanded Disability Status Scale), active magnetic resonance imaging (MRI) lesions (defined as new/enlarging T2 lesions), and brain atrophy (defined as change in brain volume between month 24 and month 6-12). Treatment effects were expressed as relative reductions. A linear regression, weighted for trial size and duration, was used to assess the relationship between the treatment effects on MRI markers and on disability progression. RESULTS: Thirteen trials including >13,500 RRMS patients were included in the meta-analysis. Treatment effects on disability progression were correlated with treatment effects both on brain atrophy (R(2) = 0.48, p = 0.001) and on active MRI lesions (R(2) = 0.61, p < 0.001). When the effects on both MRI endpoints were included in a multivariate model, the correlation was higher (R(2) = 0.75, p < 0.001), and both variables were retained as independently related to the treatment effect on disability progression. INTERPRETATION: In RRMS, the treatment effect on brain atrophy is correlated with the effect on disability progression over 2 years. This effect is independent of the effect of active MRI lesions on disability; the 2 MRI measures predict the treatment effect on disability more closely when used in combination.
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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 | 0.055 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".