Incidence trends in pediatric central nervous system tumors in Canada: a 15 years report from Cancer and Young People in Canada (CYP-C) registry
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The aim of this study is to present a national surveillance report on pediatric central nervous system (CNS) tumors in Canada during the period between 2001 and 2015. METHODS: All pediatric patients with a diagnosis of primary CNS tumors were collected by the Cancer in Young People in Canada (CYP-C) surveillance system that includes every patient less than 15 years of age with a tumor seen in one of the 17 pediatric oncology centres in Canada. This registry included malignant and benign CNS tumors. We calculated the age-adjusted incidence rates (AAIRs) per 100 000 person-years for CNS tumors overall and by age group, major histology subgroups, and geographical distribution over the country. RESULTS: Overall, 3306 patients less than 15 years old had been diagnosed with a CNS tumor in Canada in 2001-2015 with a 1.23:1 male to female ratio. The overall AAIR is 3.80. The three most frequent groups of tumors were low-grade gliomas (36.4%), high-grade gliomas (22.3%), and embryonal tumors (18.7%) with incidence rates of 1.41, 0.86, and 0.72 per 100 000 person-years, respectively. The incidence rate of pediatric CNS tumors is stable during the period 2001-2015 in Canada and no significant differences were seen between malignant and benign tumors over the country. CONCLUSIONS: These data represent all the pediatric patients 0-14 years old with a CNS tumor in the Canadian population. Incidence rates by age group, sex, and subgroups of tumors are similar to those seen in the literature.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it