A First Look at Relative Survival by Stage for Colorectal and Lung Cancers in Canada
Bibliographic record
Abstract
Monitoring and reporting on cancer survival provides a mechanism for understanding the effectiveness of Canada's cancer care system. Although 5-year relative survival for colorectal cancer and lung cancer has been previously reported, only recently has pan-Canadian relative survival by stage been analyzed using comprehensive registry data. This article presents a first look at 2-year relative survival by stage for colorectal and lung cancer across 9 provinces. As expected, 2-year age-standardized relative survival ratios (arsrs) for colorectal cancer and lung cancer were higher when the cancer was diagnosed at an earlier stage. The arsrs for stage i colorectal cancer ranged from 92.2% in Nova Scotia [95% confidence interval (ci): 88.6% to 95.1%] to 98.4% in British Columbia (95% ci: 96.2% to 99.3%); for stage iv, they ranged from 24.3% in Prince Edward Island (95% ci: 15.2% to 34.4%) to 38.8% in New Brunswick (95% ci: 33.3% to 44.2%). The arsrs for stage i lung cancer ranged from 66.5% in Prince Edward Island (95% ci: 54.5% to 76.5%) to 84.8% in Ontario (95% ci: 83.5% to 86.0%). By contrast, arsrs for stage iv lung cancer ranged from 7.6% in Manitoba (95% ci: 5.8% to 9.7%) to 13.2% in British Columbia (95% ci: 11.8% to 14.6%). The available stage data are too recent to allow for meaningful comparisons between provinces, but over time, analyzing relative survival by stage can provide further insight into the known differences in 5-year relative survival. As the data mature, they will enable an assessment of the extent to which interprovincial differences in relative survival are influenced by differences in stage distribution or treatment effectiveness (or both), permitting targeted measures to improve population health outcomes to be implemented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".