Trends in the Epidemiology and Treatment of Pediatric-Onset Multiple Sclerosis in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Fingolimod became the first disease-modifying therapy approved by Health Canada for pediatric multiple sclerosis in 2018, but the impact of that approval on treatment patterns in Canada is unknown. The aim of this study was to describe trends in the epidemiology and treatment of pediatric-onset multiple sclerosis in Alberta, Canada. METHODS: This study entailed a retrospective review of administrative health databases using 2 case definitions of multiple sclerosis. Those <19 years of age at a date of diagnosis between January 1, 2011, and December 31, 2020, were included. Incidence and prevalence estimates were calculated and stratified by sex and age cohort. Pharmacy dispenses of disease-modifying therapies were identified. RESULTS: 106 children met one or both case definitions. In 2020, the age-standardized incidence using the 2 case definitions was 0.47 and 0.57 per 100 000, and the age-standardized prevalence was 2.84 and 3.41 per 100 000, respectively. Seventy-nine incident cases were identified, 38 (48%) of whom were dispensed a disease-modifying therapy prior to age 19 years. Injectables accounted for all initial pediatric disease-modifying therapy dispenses prior to 2019, whereas in 2019-2020 injectables accounted for only 3 of 15 (20%) initial dispenses, and instead B-cell therapies were the most common initial disease-modifying therapy (6 of 15, 40%). In 2020, B-cell therapies were the most common disease-modifying therapy dispensed overall (9 of 22 dispenses, 41%) followed by fingolimod (6 of 22, 27%). CONCLUSION: The treatment of children with multiple sclerosis in Alberta has evolved, with a rapid shift in 2019 away from injectables to newer agents, although B-cell therapies-not fingolimod-are now most commonly dispensed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".