P.164 The Rising Incidence and Prevalence of Brain Tumours: a Canadian epidemiological study
Bibliographic record
Abstract
Background: Primary malignant brain tumours account for over one third of all brain tumours and are associated with high morbidity and mortality. The purpose of this paper is to estimate the rate and trends of incidence and prevalence for primary malignant CNS tumours in Canada from 1992 to 2017. Methods: An epidemiological study using publicly available data from Statistics Canada: Canadian Cancer Registry (CCR) from 1992 to 2017 for all of Canada was conducted. Incidence and prevalence per 100,000, age-standardized incidence, and age-standardized prevalence per 100,000 person-years of primary malignant CNS tumours were calculated and stratified by sex and age: pediatric (0-19), adult (20-64), and elderly (>64) populations. Results: During the study period, incidence and prevalence increased by 27.3% and 28.8%, respectively. Males accounted for 56% of all diagnoses and experienced decreased survival compared to females one year after diagnosis (p-value = 0.04). Age-standardized rates of incidence and prevalence were highest in elderly populations. Conclusions: Overall, the incidence of primary malignant CNS tumours has increased from 1992 to 2017 with males and the elderly disproportionately affected. Increased healthcare resources and awareness are needed to better identify and deliver evidence-based care for these patients.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".