High‐Dose Pulse Glucocorticoid Treatment Prevents White Matter Spinal Cord Pseudoatrophy in Newly Diagnosed Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Spinal cord (SC) atrophy correlates with and predicts the underlying progressive biology in active and non-active multiple sclerosis (MS), thereby providing a biomarker for clinical trials and patient management. Initiation of disease-modifying therapy (DMT) may be followed by early pronounced central nervous system (CNS) volume loss due to resolution of inflammation (pseudoatrophy) and confounding the interpretation of atrophy. High-dose glucocorticoids (HDGs) reduce inflammation and might therefore modify pseudoatrophy. METHODS: One hundred twenty-three newly diagnosed and DMT-naïve MS participants (relapsing-remitting, 70% female participants, median age = 36 years, Expanded Disability Status Scale [EDSS] 2.0) were followed for up to 3 years. Forty-two participants received HDG before baseline magnetic resonance imaging (MRI; DMT-HDG; median = 52 days, interquartile range [IQR] = 37-71), whereas 60 did not (DMT/no-HDG). Twenty-one participants remained untreated (no-DMT), and 102 started DMT after baseline MRI. SC total cervical cord cross-sectional area (TCA), gray matter area (GMA), and white matter area (WMA) and regional brain volumes were analyzed using mixed effects models. RESULTS: The DMT-HDG, DMT/no-HDG, and no-DMT groups had similar demographic, clinical, and radiological features. Pronounced SC pseudoatrophy was observed based on more year 1 versus year 2 volume loss for DMT/no-HDG (-2.06% vs. 0.83%; P = 0.02) but not DMT-HDG (-0.51% vs. 0.66%; P = 0.8) and more year 1 volume loss for DMT/no-HDG compared to DMT-HDG (-2.06% vs. 0.51%; P = 0.02). INTERPRETATION: HDG preceding baseline MRI suppresses CNS white matter (WM) pseudoatrophy after DMT initiation, most conspicuously for the SC. Suppression of pseudoatrophy with HDG may improve the fidelity of clinical trials and enhance the feasibility for short-term trials with SC and brain MRI outcomes in active MS by pretreatment with HDG. ANN NEUROL 2025;98:837-850.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".