Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing–remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: 'No evidence of disease activity' (NEDA), defined as absence of magnetic resonance imaging activity (T2 and/or gadolinium-enhanced T1 lesions), relapses and disability progression ('NEDA-3'), is used as a comprehensive measure of treatment response in relapsing multiple sclerosis (RMS), but is weighted towards inflammatory activity. Accelerated brain volume loss (BVL) occurs in RMS and is an objective measure of disease worsening and progression. OBJECTIVE: To assess the contribution of individual components of NEDA-3 and the impact of adding BVL to NEDA-3 ('NEDA-4') METHODS: We analysed data pooled from two placebo-controlled phase 3 fingolimod trials in RMS and assessed NEDA-4 using different annual BVL mean rate thresholds (0.2%-1.2%). RESULTS: At 2 years, 31.0% (217/700) of patients receiving fingolimod 0.5 mg achieved NEDA-3 versus 9.9% (71/715) on placebo (odds ratio (OR) 4.07; p < 0.0001). Adding BVL (threshold of 0.4%), the respective proportions of patients achieving NEDA-4 were 19.7% (139/706) and 5.3% (38/721; OR 4.41; p < 0.0001). NEDA-4 status favoured fingolimod across all BVL thresholds tested (OR 4.01-4.41; p < 0.0001). CONCLUSION: NEDA-4 has the potential to capture the impact of therapies on both inflammation and neurodegeneration, and deserves further evaluation across different compounds and in long-term studies.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".