Incidence and survival of primary central nervous system tumors diagnosed in 4 Canadian provinces from 2010 to 2015
Bibliographic record
Abstract
Background: The Brain Tumor Registry of Canada was established in 2016 to enhance infrastructure for surveillance and clinical research on Central Nervous System (CNS) tumors. We present information on primary CNS tumors diagnosed among residents of Canada from 2010 to 2015. Methods: Data from 4 provincial cancer registries were analyzed representing approximately 67% of the Canadian population. Age-standardized incidence rates (ASIR) and 95% confidence intervals (CI) were calculated using the 2011 Canadian population age distribution. Net survival was estimated using the Pohar-Perme method. Results: A total of 31 644 primary tumors were identified for an ASIR of 22.8 per 100 000 person-years. Nonmalignant tumors made up 47.1% of all classified tumors, with mixed behaviors present in over half of histology groupings. Unclassified were 19.5% of all tumors. The most common histological subtypes are meningiomas (ASIR = 5.5 per 100 000 person-years); followed by glioblastomas (ASIR 4.0 per 100 000 person-years). The overall 5-year net survival rate for CNS tumors was 65.5%; females 70.2% and males 60.4%. GBMs continue to be the most lethal CNS tumors for all sex and age groups. Conclusions: The low annual frequency of most CNS tumor subtypes emphasizes the value of population-based data on all primary CNS tumors diagnosed among Canadians. The large number of histological categories including mixed behaviors and the proportion of unclassified tumors emphasizes the need for complete reporting. Variation in incidence and survival across histological groups by sex and age highlights the need for comprehensive and histology-specific reporting. These data can be used to better inform research and health system planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".