Incidence of acquired demyelination of the CNS in Canadian children
Bibliographic record
Abstract
BACKGROUND: The incidence of acquired demyelination of the CNS (acquired demyelinating syndromes [ADS]) in children is unknown. It is important that physicians recognize the features of ADS to facilitate care and to appreciate the future risk of multiple sclerosis (MS). OBJECTIVE: To determine the incidence, clinical features, familial autoimmune history, and acute management of Canadian children with ADS. METHODS: Incidence and case-specific data were obtained through the Canadian Pediatric Surveillance Program from April 1, 2004, to March 31, 2007. Before study initiation, a survey was sent to all pediatric health care providers to determine awareness of MS as a potential outcome of ADS in children. RESULTS: Two hundred nineteen children with ADS (mean age 10.5 years, range 0.66-18.0 years; female to male ratio 1.09:1) were reported. The most common presentations were optic neuritis (ON; n = 51, 23%), acute disseminated encephalomyelitis (ADEM; n = 49, 22%), and transverse myelitis (TM; n = 48, 22%). Children with ADEM were more likely to be younger than 10 years, whereas children with monolesional ADS (ON, TM, other) were more likely to be older than 10 years (p < 0.001). There were 73 incident cases per year, leading to an annual incidence of 0.9 per 100,000 Canadian children. A family history of MS was reported in 8%. Before study initiation, 65% of physicians indicated that they considered MS as a possible outcome of ADS in children. This increased to 74% in year 1, 81% in year 2, and 87% in year 3. CONCLUSION: The incidence of pediatric acquired demyelinating syndromes (ADS) is 0.9 per 100,000 Canadian children. ADS presentations are influenced by age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".