Diffusion tensor magnetic resonance imaging in very early onset pediatric multiple sclerosis
Bibliographic record
Abstract
OBJECTIVES: Active myelination during childhood may influence the impact of multiple sclerosis (MS) on brain structural integrity. We studied normal-appearing white matter (NAWM) in children with MS onset before age 12 years using diffusion tensor (DT) magnetic resonance imaging (MRI). METHODS: DT MRI scans were obtained from 22 MS children with their first attack before age 12 years, and 31 healthy controls from two referral centers. Using probabilistic tractography, brain tissue integrity within interhemispheric, intrahemispheric, and projection tracts was compared between patients and site-matched controls. The impact of disease and age at MRI on tract NAWM fractional anisotropy (FA) and mean diffusivity (MD) values was evaluated using linear models. RESULTS: Compared to controls, pediatric MS patients had reduced FA and increased MD of the bilateral superior longitudinal fasciculus and corpus callosum (CC), without center-by-group interaction. CC NAWM average FA was correlated with brain T2 lesion volume. In controls, the majority of the tracts analyzed showed a significant increase of FA and decrease of MD with age. Such a linear correlation was lost in patients. CONCLUSIONS: In very young pediatric MS patients, DT MRI abnormalities affect brain WM tracts differentially, and are only partially correlated with focal WM lesions. Impaired maturation of WM tracts with age may be an additional factor contributing to these findings.
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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 | 0.002 |
| 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) | 0.001 | 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".