High rates of health care utilization in pediatric multiple sclerosis: A Canadian population-based study
Bibliographic record
Abstract
We aimed to compare health care utilization of children with pediatric-onset multiple sclerosis to that of age, sex and geographically-matched children without multiple sclerosis. Using population-based administrative data from Ontario, Canada for the period 2003-2014, we applied a validated case definition to identify persons aged ≤18 years with multiple sclerosis. We identified up to 5 children without multiple sclerosis matched on sex, age, and region of residence. In each cohort, we determined annual rates of any hospitalization and physician services use. Using general linear models we compared utilization rates adjusting for age, sex, region, socioeconomic status and year. Subsequently, we limited the analysis to incident cases of multiple sclerosis and their matches, and compared rates of utilization in the year of multiple sclerosis diagnosis, and the three years thereafter. We identified 659 youth with multiple sclerosis (428 incident cases), and 3,294 matched controls. Two-thirds of both cohorts were female. After adjustment for sociodemographic factors and year, the multiple sclerosis cohort was more likely to be hospitalized than the matched cohort (odds ratio 15.2; 95%CI: 12.0, 19.1), and had higher rates of ambulatory physician visits (rate ratio 4.58; 95%CI: 4.26, 4.92). The odds of hospitalization (odds ratio 40.1; 95%CI: 27.1, 59.5) and physician visits (rate ratio 5.14; 95%CI: 4.63, 5.71) were markedly elevated in the year of MS diagnosis, declining thereafter but remaining elevated versus the matched cohort. Children with multiple sclerosis have substantially elevated rates of health care utilization as compared to matched children without multiple sclerosis, over calendar time and throughout the early disease course.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".