EPID-02. MALIGNANT PRIMARY BRAIN AND OTHER CENTRAL NERVOUS SYSTEM TUMOURS DIAGNOSED AMONG THE CANADIAN PAEDIATRIC POPULATION FROM 2009 TO 2013
Bibliographic record
Abstract
The Canadian Brain Tumour Registry (CBTR) project was established in 2016 with the aim of enhancing infrastructure for surveillance and clinical research to improve health outcomes for brain tumour patients in Canada. We present a national surveillance report on malignant primary brain and central nervous system (CNS) tumours diagnosed in the Canadian paediatric population from 2009–2013. Patients aged 0–19 years were identified through the Canadian Cancer Registry (CCR); an administrative dataset that includes cancer incidence data from all provinces/territories in Canada. Cancer diagnoses are coded using the ICD-O3 system. Tumour types were classified by site and histology using the Central Brain Tumour Registry of the United States classification; and the International Classification of Childhood Cancer (ICCC). Incidence rates (IR) and 95% confidence intervals (CI) were calculated per 100,000 persons and standardized to the 2011 census population. Overall, 1,130 malignant brain and CNS tumours were diagnosed in the Canadian paediatric population from 2009–2013 (IR: 2.90; 95%CI: 2.74,3.08). Of these, 625 were diagnosed among males (IR: 3.14; 95%CI: 2.89,3.39) and 505 among females (IR: 2.66; 95%CI: 2.43,2.90). The most common ICCC classification was III(b)-Astrocytomas (IR: 1.05; 95%CI: 0.95,1.16). The most common histology by age group was: 0–4 years: Embryonal tumours (IR: 1.07; 95%CI: 0.88,1.30); 5–9 years: Pilocytic Astrocytoma (IR: 0.88; 95%CI: 0.71,1.10); 10–14 years: Pilocytic Astrocytoma (IR: 0.62; 95%CI: 0.49,0.80); and 15–19 years: Pilocytic Astrocytoma (IR: 0.41; 95%CI: 0.31,0.55). The presentation will include: IRs for both classifications, histology groups, geographic distribution and a comparison between Canada and the United States.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".