REGIONAL DIFFERENCES IN THE SURVIVAL EXPERIENCE OF PATIENTS WITH CENTRAL NERVOUS SYSTEM TUMOURS IN CANADA
Bibliographic record
Abstract
Abstract Health care in Canada is delivered on a provincial or territorial level. The objective of our population-based study was to investigate regional differences in survival among Canadians diagnosed with central nervous system (CNS) tumours. We identified 50,670 patients diagnosed with a first-ever primary CNS tumour between 2008 and 2017 with follow-up until December 31, 2017 (excluding Quebec) from the Canadian Cancer Registry linked to vital statistics. We selected the four highest incidence histologies and used Cox proportional hazards regression to estimate hazard ratios (HRs) for regions in Canada (British Columbia, the Prairie provinces, Ontario, the Atlantic provinces, and the Territories) adjusting for sex and tumour behaviour (malignant vs. non-malignant), and stratified by patient age. Ontario was the reference region and had the best survival profile for all histologies investigated. The Atlantic provinces had the highest HR for glioblastomas (HR=1.26, 95% CI:1.18-1.35), gliomas not otherwise specified (NOS) (Overall: HR=1.87, 95% CI:1.43-2.43; Pediatric population: HR=2.86, 95% CI:1.28-6.39) and unclassified tumours (HR=1.95, 95% CI:1.63-2.34). For meningiomas, the Territories had the highest HR (HR=2.44, 95% CI:1.09-5.45) followed by the Prairie provinces (HR=1.52, 95% CI:1.38-1.67). Our findings suggest that regional differences in survival may exist for patients with specific histological subtypes of CNS tumours at the population level. Whether the differential capture of non-malignant tumours across regions, tumour misclassification, or both contributes to the observed regional survival differences warrants further investigation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| 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".