A 24-month advanced magnetic resonance imaging study of multiple sclerosis patients treated with alemtuzumab
Bibliographic record
Abstract
BACKGROUND: Tissue damage in both multiple sclerosis (MS) lesions and normal-appearing white matter (NAWM) are important contributors to disability and progression. Specific aspects of MS pathology can be measured using advanced imaging. Alemtuzumab is a humanised monoclonal antibody targeting CD52 developed for MS treatment. OBJECTIVE: To investigate changes over 2 years of advanced magnetic resonance (MR) metrics in lesions and NAWM of MS patients treated with alemtuzumab. METHODS: and myelin water fraction (MWF) were determined for stable lesions, new lesions and NAWM. Fractional anisotropy was calculated for the corpus callosum (CC) and N-acetylaspartate (NAA) concentration was determined from a large NAWM voxel. Brain parenchymal fraction (BPF), cortical thickness and CC area were also calculated. RESULTS: No change in any MR measurement was found in lesions or NAWM over 24 months. BPF, cortical thickness and CC area all showed decreases in the first year followed by stability in the second year. CONCLUSION: Advanced MR biomarkers of myelin (MWF) and neuron/axons (NAA) show no change in NAWM over 24 months in alemtuzumab-treated MS participants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".