OS11 - 165 Brain Cancer Survival and Conditional Survival Rates in Canada (1992-2008)
Bibliographic record
Abstract
To investigate patterns of survival and estimate conditional survival rates among brain cancer patients in Canada. METHODS: Canadian Cancer Registry data were obtained for all patients with primary brain cancer diagnosed between 1992 and 2008 (n=38,095). Follow-up ended with patient death or December 31, 2008, whichever occurred first. Crude Kaplan-Meier estimates were calculated at one, two, and five years post-diagnosis and also used to estimate conditional survival (restricted to 2000-2008). Age group, sex, residence and microscopic confirmation were considered in estimating rates for major histology types using multivariate models. RESULTS: The overall five-year survival rate was 27%. Oligodendrogliomas had the highest 5-year survival rate (65%, 95% CI: 62.5-67.4%) and glioblastomas the lowest (4.0%, 95% CI: 3.7-4.3%). Compared to Ontario, the age- and sex-adjusted 5-year glioblastoma survival estimates were lower in British Columbia, Alberta and Manitoba-Saskatchewan, lower in all other regions for diffuse astrocytoma, and lower in Manitoba-Saskatchewan for anaplastic astrocytomas. Estimates were significantly higher for oligodendrogliomas in Alberta, and for anaplastic oligodendrogliomas in Alberta and Quebec (P<0.05). Longer term conditional survival rates (surviving an additional 2 years 1-4 years after diagnosis) varied by histologic group. CONCLUSION: There is a need to further explore the underlying reasons for the observed variation in survival rates by region in an effort to improve the prognosis of brain cancer in the Canadian patient population. Conditional survival information has value for clinicians as they plan the course of treatment and follow-up for individual patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".