Presence of slowly expanding lesions in multiple sclerosis predicts progressive demyelination within lesions and normal-appearing tissue over time
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) slowly expanding lesions (SELs) are defined on magnetic resonance imaging (MRI) as contiguous regions of pre-existing focal non-contrast-enhancing T2 lesions with constant and concentric local expansion on conventional T1-weighted and T2-weighted images. SELs are associated with an increased risk of disability progression. METHODS: Myelin-related changes detected using myelin water fraction (MWF) and magnetisation transfer ratio (MTR) in SELs and T2 lesions were measured over 192 weeks in participants with relapsing MS. RESULTS: In participants with SELs (SEL+), SELs (MWF: 0.12 ± 0.03, MTR: 33.1 ± 3.6 pu) showed reduced myelin measures at baseline compared to T2 lesions (MWF: 0.13 ± 0.02, MTR: 35.1 ± 2.4 pu). In participants without SELs (SEL-), T2 lesions had higher myelin measures (MWF: 0.15 ± 0.02, MTR: 36.2 ± 2.0 pu) compared to T2 lesions in SEL+. Over 4 years, only SELs showed decreases in MWF (-11.4%). The percentage of abnormal voxels within normal-appearing white matter was higher in SEL+ and increased over time (SEL+ MWF Week 0: 0.56%, Week 192: 0.98%; SEL- MWF Week 0: 0.13%, Week 192: 0.25%). CONCLUSION: Our results indicate progressive focal and global demyelination in SEL+ participants and that the presence of SELs might be a biomarker for participants with ongoing diffuse or smouldering inflammation within the whole brain.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".