Pediatric central nervous system inflammatory demyelination: acute disseminated encephalomyelitis, clinically isolated syndromes, neuromyelitis optica, and multiple sclerosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: We review the recent consensus definitions for acute disseminated encephalomyelitis,clinically isolated syndromes, neuromyelitis optica, and multiple sclerosis (MS) in children. We also discuss the importance of clinically defined consistency, the need for biomarker-based patient delineation, the likelihood of subsequent MS diagnosis following acute demyelination, and current therapeutic options. RECENT FINDINGS: Studies of children after a first episode of demyelination have identified disease onset in adolescence, intrathecal oligoclonal bands and optic neuritis as associated with a higher MS risk, whereas prepubertal onset, presence of polyfocal features with encephalopathy, and transverse myelitis have been associated with a lower risk of subsequent MS. The relapsing-remitting form of MS accounts for over 96% of all MS in children. Neuromyelitis optica appears to be a distinct clinical and biological entity for which neuromyelitis optica IgG provides a high degree of specificity. Neuroimaging plays a key role in the diagnosis of acute demyelination, and serial imaging can provide evidence of lesion dissemination in time that can confirm a diagnosis of MS even in the absence of clinical relapse. SUMMARY: Although clinical definitions, increased awareness, and MRI have contributed to the increasing identification of acute demyelination and MS in children, challenges remain in predicting MS risk. Identification of reliable biomarkers or application of more advanced neuroimaging techniques would serve as invaluable tools to distinguish monophasic demyelination from the first attack of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".