Clinical features of children and adolescents with multiple sclerosis
Bibliographic record
Abstract
There is increasing appreciation that multiple sclerosis (MS) can begin in childhood or adolescence, but pediatric MS continues to be a rare entity, with an estimated 2 to 5% of patients with MS experiencing their first clinical symptoms before age 16. A prompt diagnosis of pediatric MS is important to optimize overall management of both the physical and social impact of the disease. The widespread use of disease-modifying therapies (DMT) for MS in adults, as early as following an initial isolated episode, has led to the use of DMT in children and adolescents with MS. However, it is imperative to distinguish pediatric MS from other childhood CNS inflammatory demyelinating disorders such as acute disseminated encephalomyelitis. Although increasing evidence suggests a slower disease course in children with MS compared to adults, significant disability can still accumulate by early adulthood. Furthermore, associated neurocognitive deficits can impair both academic and psychosocial function at a critical juncture in a young person's life. This article reviews the clinical characteristics, neuroimaging, paraclinical findings, disease course, epidemiology, genetics, and pathophysiology of pediatric MS vis-à-vis adult MS. Further research of pediatric MS may advance our understanding of MS pathophysiology in general, as well as improve the long-term health care outcomes of children and adolescents diagnosed 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".