ED-04 * SURVIVAL RATES AMONG CANADIAN PATIENTS WITH PRIMARY MALIGNANT BRAIN TUMOURS: AN ANALYSIS BASED ON STATISTICS CANADA DATA, 1992-2010
Bibliographic record
Abstract
BACKGROUND: There is little information availableon brain tumour survival among the Canadian population. The objective is to investigate patterns of survival among patients with malignant brain tumours in Canada by province/territory, age at diagnosis, histology, and time period. We aim to examine whether outcomes have improved over time and whether they are consistent with those reported in the US. MATERIALS AND METHODS: Data from the Canadian Cancer Registry are available through Statistics Canada. Data on all primary brain tumours diagnosed between 1992-2010 have been requested for analysis. Analysis will be restricted to patients with no previous history of cancer and no subsequent development of second primaries. Survival curves will be estimated where there are at least 20 events to provide precision to the estimates given that these are rare tumours. Overall and stratified survival rates (1, 2, 5 and 10 year) by province/territory, age at diagnosis, histology and time period and 95% confidence intervals will be estimated. Standard Kaplan Meier and Proportional Hazards survival analysis techniques will be performed to calculate survival curves, using SAS software. RESULTS: New information to health care providers and decision makers will be presented. CONCLUSION: These data will provide a pan-Canadian picture of primary malignant brain tumour survival over time; including overall and subgroup estimates which will be compared with published rates from the Central Brain Tumour Registry of the US. Identified areas for improvement will potentially influence brain tumour management.
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".