Evolution of regional brain atrophy in children with multiple sclerosis
Bibliographic record
Abstract
While multiple sclerosis (MS) has classically been considered to be a white matter disease, it is now clear that gray matter changes are seen at onset. Importantly, regional gray matter atrophy correlates strongly with motor outcomes in adult patients.1 Individuals with pediatric-onset MS have greater disease burden, as evidenced by higher relapse rate2 and increased lesion volume and atrophy on MRI3 than those with adult-onset MS. Furthermore, cognitive decline may be seen as early as 2 years after diagnosis in this population.4 Importantly, studies of structural correlates of disease progression in pediatric-onset MS must take the dynamic and maturational changes known to occur in the pediatric brain into account, including age- and sex-specific growth in some areas and regression and pruning in others.5 To this end, previous studies focused on white matter tracts and head size in pediatric-onset MS, and showed alterations in growth trajectories in patients with pediatric-onset MS in comparison with healthy youth.6 Others have demonstrated the extent of gray matter injury in the pediatric MS population, but have largely focused on specific deep gray matter structures.7 Much less is known about the dynamic pattern of growth and regression of various gray matter regions, and their relationship to outcomes in the pediatric patients with MS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".