Central nervous system atrophy predicts future dynamics of disability progression in a real‐world multiple sclerosis cohort
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In an era of individualized multiple sclerosis (MS) patient management, biomarkers for accurate prediction of future clinical outcomes are needed. We aimed to evaluate the potential of short-term magnetic resonance imaging (MRI) atrophy measures and serum neurofilament light chain (sNfL) as predictors of the dynamics of disability accumulation in relapse-onset MS. METHODS: Brain gray and white matter, thalamic, striatal, pallidal and cervical spinal cord volumes, and lesion load were measured over three available time points (mean time span 2.24 ± 0.70 years) for 183 patients (140 relapsing-remitting [RRMS] and 43 secondary-progressive MS (SPMS); 123 female, age 46.4 ± 11.0 years; disease duration 15.7 ± 9.3 years), and their respective annual changes were calculated. Baseline sNfL was also measured at the third available time point for each patient. Subsequently, patients underwent annual clinical examinations over 5.4 ± 3.7 years including Expanded Disability Status Scale (EDSS) scoring, the nine-hole peg test and the timed 25-foot walk test. RESULTS: Higher annual spinal cord atrophy rates and lesion load increase predicted higher future EDSS score worsening over time in SPMS. Lower baseline thalamic volumes predicted higher walking speed worsening over time in RRMS. Lower baseline gray matter, as well as higher white matter and spinal cord atrophy rates, lesion load increase, baseline striatal volumes and baseline sNfL, predicted higher future hand dexterity worsening over time. All models showed reasonable to high prediction accuracy. CONCLUSION: This study demonstrates the capability of short-term MRI metrics to accurately predict future dynamics of disability progression in a real-world relapse-onset MS cohort. The present study represents a step towards the utilization of structural MRI measurements in patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".