Measuring progress in cancer survival across Canadian provinces: Extending the cancer survival index to further evaluate cancer control efforts
Bibliographic record
Abstract
Background: A comprehensive evaluation of progress in cancer survival for all cancer types combined in Canada has recently been accomplished. An analogous evaluation across Canadian provinces has yet to be conducted. Data and methods: Data from 1992 to 2017 are from the population-based Canadian Cancer Registry death-linked analytic file. Provincial cancer survival index (CSI) estimates were calculated as the weighted sum of the sex- and cancer-specific age-standardized provincial net survival estimates. Provincial sex-specific CSI estimates were calculated separately using sex-specific cancer type weights. Data availability (Quebec) and sufficiency (Prince Edward Island and the territories) issues precluded CSI calculations for all jurisdictions. Results: For the most recent period, 2013 to 2017, the five-year CSI was highest in Ontario (64.1%) and Alberta (63.3%), and lowest in Nova Scotia (60.8%). Significant progress in the five-year CSI since the period from 1992 to 1996 was observed in each province; the largest increases occurred in Alberta (8.7 percentage points) and Ontario (8.6 percentage points). Alberta's increase improved its relative provincial ranking from eighth to second. The influence of prostate cancer on provincial changes in the CSI since the period from 2003 to 2007 varied considerably from strongly counterproductive in New Brunswick, Saskatchewan and Nova Scotia because of decreasing prostate cancer survival, to strongly productive in Manitoba. Interpretation: Significant progress has been made in five-year cancer survival for all cancers combined since the early 1990s in each Canadian province studied. However, the magnitude of the progress has not been uniform across the provinces, and the cancer and sex combinations that have most influenced it have varied by province and period.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".