Brain Cancer Survival in Canada 1996–2008: Effects of Sociodemographic Characteristics
Bibliographic record
Abstract
Background: Literature suggests that factors such as rural residence and low socioeconomic status (ses) might contribute to disparities in survival for Canadian cancer patients because of inequities in access to care. However, evidence specific to brain cancer is limited. The present research estimates the effects of rural or urban residence and ses on survival for Canadian patients diagnosed with brain cancer. Methods: (3rd edition) site and histology codes. Hazard ratios (hrs) and 95% confidence intervals (cis) were estimated using Cox proportional hazards models. Events were restricted to individuals whose underlying cause of death was cancer-related. Postal codes were used to match patient records with Statistics Canada data for rural or urban residence and neighbourhood income as a surrogate measure of ses. Results: Of 25,700 patients included in the analysis, 78% died during the study period, 21% lived in rural areas, and 19% were in the lowest income group. A modest variation in survival by rural compared with urban residence was observed for patients with glioblastoma (first 5 weeks after diagnosis hr: 0.86; 95% ci: 0.79 to 0.99) and oligoastrocytoma (first 3 years after diagnosis hr: 1.41; 95% ci: 1.03 to 1.93). Small effects of low compared with high income were seen for patients with glioblastoma (first 1.5 years after diagnosis hr: 1.15; 95% ci: 1.08 to 1.22) and diffuse astrocytoma (first 6 months after diagnosis hr: 1.17; 95% ci: 1.00 to 1.36). Conclusions: Our analysis did not yield evidence of strong effects of rural compared with urban residence or ses strata on survival in brain cancer. However, some variation in survival for patients with specific histologies warrants further research into the mechanisms by which rural or urban residence and income stratum influences survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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".