Brain volume loss in relapsing multiple sclerosis: indirect treatment comparisons of available disease-modifying therapies
Bibliographic record
Abstract
BACKGROUND: Brain volume loss (BVL) has been identified as a predictor of disability progression in relapsing multiple sclerosis (RMS). As many available disease-modifying treatments (DMTs) have shown an effect on slowing BVL, this is becoming an emerging clinical endpoint in RMS clinical trials. METHODS: In this study, a systematic literature review was conducted to identify BVL results from randomized controlled trials of DMTs in RMS. Indirect treatment comparisons (ITCs) were conducted to estimate the relative efficacy of DMTs on BVL using two approaches: a model-based meta-analysis (MBMA) with adjustment for measurement timepoint and DMT dosage, and a network meta-analysis (NMA). RESULTS: In the MBMA, DMTs associated with significantly reduced BVL versus placebo at two years included fingolimod (mean difference [MD] = 0.25; 95% confidence interval [CI] = 0.15 - 0.36), ozanimod (MD = 0.26; 95% CI = 0.12 - 0.41), teriflunomide (MD = 0.38; 95% CI = 0.20 - 0.55), alemtuzumab (MD = 0.38; 95% CI = 0.10 - 0.67) and ponesimod (MD = 0.71; 95% CI = 0.48 - 0.95), whereas interferons and natalizumab performed the most poorly. The results of NMA analysis were generally comparable with those of the MBMA. CONCLUSIONS: Limitations of these analyses included the potential for confounding due to pseudoatrophy, and a lack of long-term clinical data for BVL. Our findings suggest that important differences in BVL may exist between DMTs. Continued investigation of BVL in studies of RMS is important to complement traditional disability endpoints, and to foster a better understanding of the mechanisms by which DMTs can slow BVL.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.038 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".