PC3 - 164 Urban-Rural Residence and Brain Cancer Survival in Canada (1996-2008)
Bibliographic record
Abstract
Disparities in cancer survival rates have been identified for rural patients in Canada and are thought to be due to inequities in access to care. The objective was to perform the first examination of urban and rural brain cancer survival in Canada. Methods: A population-based retrospective cohort study was performed using Canadian Cancer Registry data for patients diagnosed with a primary brain cancer from 1996-2008. Seven major brain cancer histology groups used were glioblastoma, diffuse astrocytoma, glioma (not otherwise specified), oligodendroglioma, anaplastic astrocytoma, oligoastrocytic tumours, and anaplastic oligodendroglioma as categorized by the Central Brain Tumor Registry of the United States (CBTRUS). Kaplan-Meier (KM) survival estimates and Cox Proportional Hazards Regression were performed, adjusting for sex, histology, age group, region, and urban-rural residence. Rural residence was defined using Statistics Canada’s “Rural and Small Town” definition of living in a region with a population of less than 10,000 people. Results: No significant difference between urban and rural residence was identified in crude KM survival estimates. Though not significant, 5-year survival was generally better among rural residents than urban residents, except for rural residents with anaplastic astrocytoma. There remained no significant difference for Cox hazard ratios after adjustment for age, sex, or region. Conclusions: This is the first study to examine the effect of urban-rural residence on brain cancer survival. No significant differences for any histology were found, indicating equitable access to care for brain cancer patients in Canada, regardless of their location of residence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".