Projecting the Future Prevalence of Childhood Cancer In Ontario using Microsimulation Modeling
Bibliographic record
Abstract
Children diagnosed with cancer have lifelong health care needs. The prevalence of childhood cancer has been steadily increasing in Ontario, though no prevalence projections exist to anticipate future health care system demands. A population-based, open-cohort microsimulation model was constructed to project the limited-duration prevalence of childhood cancer in Ontario, by cancer type, for years 2020-2040. Model inputs were derived from health administrative databases, provincial population data sources, and external literature. Beginning with 1970, the model population was updated annually with births, deaths, net migration, and incident cases of childhood cancer. Fifty Monte Carlo simulations were run to vary model inputs and generate median health outcomes with 95% credible intervals (CI). Between 2020 and 2040, annual incidence counts are projected to increase by 33%. In 2040, 25171 (95% CI: 24267-26975) individuals are projected to reside in Ontario who were diagnosed in 1970 or later, 87% of which will be 5-year survivors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".