Neuromyelitis optica-IgG in childhood inflammatory demyelinating CNS disorders
Bibliographic record
Abstract
OBJECTIVE: To determine seroprevalence of neuromyelitis optica (NMO)-IgG in childhood CNS inflammatory demyelinating disorders. METHODS: We analyzed demographic, clinical, and radiologic data in a blinded fashion and assessed serum NMO-IgG status for 87 children: 41 with relapsing-remitting multiple sclerosis (RRMS), 17 with NMO, 13 with monophasic/recurrent optic neuritis (ON), 13 with transverse myelitis, of whom 10 were longitudinally extensive on MRI spine (LETM), and another 3 with LETM in the context of acute disseminated encephalomyelitis (ADEM). RESULTS: Ten of the 87 children (11%) were seropositive. Eight of 17 with NMO (47%) were seropositive (7 of 9 with relapsing NMO [78%], 1 of 8 with monophasic NMO [12.5%]). Two other children were seropositive: 1 of 5 with recurrent ON and one child with recurrent LETM. No seropositive case was identified among 41 with RRMS (14% of whom had LETM at some point in their clinical course), 8 with monophasic ON, 9 with monophasic LETM, or 3 with LETM in the context of ADEM. CONCLUSIONS: The similar frequency of neuromyelitis optica (NMO)-IgG in both childhood and adult cases of NMO, and its rarity in relapsing-remitting multiple sclerosis, supports the concept that these diseases have a similar pathogenesis in childhood and adulthood. It is noteworthy that none of nine children with monophasic longitudinally extensive transverse myelitis (LETM) was NMO-IgG-seropositive. Furthermore, LETM does not appear to be as predictive of an NMO spectrum disorder in children as it is in adults. Longitudinal studies of larger pediatric LETM cohorts are required to ascertain whether the absence of NMO-IgG is a negative predictor for relapse in this childhood entity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".