Brain atrophy and disability worsening in primary progressive multiple sclerosis: insights from the <scp>INFORMS</scp> study
Bibliographic record
Abstract
Abstract Objective To investigate the relationship between brain volume and disability worsening over ≥3 years in the natural history of primary progressive multiple sclerosis using data from the placebo group of the INFORMS trial (n = 487; clinicaltrials.gov NCT00731692). Methods Magnetic resonance imaging scans were collected annually. Brain volume loss was determined using SIENA. Patients were stratified by baseline normalized brain volume after adjusting for demographic and disease‐burden covariates. Results Baseline normalized brain volume was predictive of disability worsening: Risk of 3‐month confirmed disability progression was reduced by 36% for high versus low baseline normalized brain volume (Cox's model hazard ratio 0.64, P = 0.0339; log‐rank test: P = 0.0297). Moreover, on‐study brain volume loss was significantly associated with disability worsening (P = 0.012) and was evident in patients with or without new lesions or relapses. Brain volume loss depended significantly on baseline T2 lesion volume (P < 0.0001). Despite low inflammatory activity at baseline (13% of patients had gadolinium‐enhancing lesions) and throughout the study (mean 0.5 new/enlarging T2 lesions and 172 mm3 T2 lesion volume increase per year), baseline T2 lesion volume was substantial (mean 10 cm3). Lower normalized brain volume at baseline correlated with higher baseline T2 volume and older age (both P < 0.0001). Interpretation Baseline brain volume and the rate of ongoing brain atrophy are significantly associated with disability worsening in primary progressive multiple sclerosis. Brain volume loss is significantly related to baseline T2 lesion volume, but partially independent of new lesion activity, which might explain the limited efficacy of anti‐inflammatory treatment.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".