Incidence trends in pediatric central nervous system tumors in Canada: a 15 years report from Cancer and Young People in Canada (CYP-C) registry
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| 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) | 0.001 | 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".