Incidence and prevalence of primary malignant brain tumours in Canada from 1992 to 2017: an epidemiologic study
Bibliographic record
Abstract
<h3>Background:</h3> Primary malignant brain tumours account for more than one-third of all brain tumours and are associated with high morbidity and mortality. The purpose of this study was to estimate the incidence and prevalence of primary malignant central nervous system (CNS) tumours and trends in these rates in Canada from 1992 to 2017. <h3>Methods:</h3> We conducted an epidemiologic study using publicly available data from the Canadian Cancer Registry from 1992 to 2017 (1994 to 2015 for prevalence) for all of Canada except Quebec (1992 to 2011). We calculated the incidence and prevalence per 100 000 person-years and the age-standardized incidence and prevalence per 100 000 person-years of primary malignant CNS tumours and stratified them by sex and age (pediatric [≥ 19 yr], adult [20–64 yr] and older adult [> 64 yr]). Our analyses assessed average disease duration, survival differences between males and females, and trends over time. <h3>Results:</h3> During the study period, the average age-standardized incidence and prevalence rates of all primary malignant CNS tumours were 7.9 and 7.6 per 100 000 person-years, respectively. The incidence and prevalence increased by 37.5% and 40.5%, respectively, over the study period. Males accounted for more than half (26 085 [56.4%]) of all diagnoses and experienced decreased survival compared to females 1 year after diagnosis (<i>p</i> = 0.048). Children accounted for 4605 new diagnoses (10.0%), adults for 23 950 (51.7%), and older adults for 17 735 (38.3%). Age-standardized incidence and prevalence rates were highest among older adults. <h3>Interpretation:</h3> Overall, the incidence of primary malignant CNS tumours increased from 1992 to 2017, and males and older adults were disproportionately affected. Increased health care resources and awareness are needed to improve identification of these tumours and deliver evidence-based care that balances safety, efficacy and preservation of quality of life for affected patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".