Mortality reduction of breast cancer in Canada: Beneficial impact of cancer care infrastructure
Bibliographic record
Abstract
649 Background: Survival after breast cancer has been improving in the last two decades. This study examines breast cancer population mortality in Canada over the last 25 years, with two main objectives: 1) To determine the impact of evolving therapy and diagnostic interventions between 1979–2003; and 2) To identify differences between the provinces of Canada, and if these could be accounted for by different cancer care infrastructures (provincial guidelines and community outreach programs, funding, etc.) Methods: Annual age-standardized mortality rates per 100,000 for breast cancer in different provinces of Canada were evaluated, using Statistics Canada Data for the years 1979–2003. Relative changes in mortality between the baseline period (average values, years 1979–83), and subsequent 5 year periods were computed for all Canada and for regions with differing organizational structure of cancer care, and expressed both as Relative % change from baseline (Mortality % Change) and as an Absolute age-standardized mortality (deaths per 100,000 population), both averaged for each of the sequential 5-year periods. Results: Conclusions: These data provide evidence for a substantial population mortality reduction in Canada, seen also in other parts of the western world. These trends probably reflect the interaction of all interventions including adjuvant systemic therapy, screening mammography, education, etc. Furthermore, in BC, the first Canadian province with implementation of provincial guidelines and well-funded community outreach since mid-1970s, the mortality reductions were observed earlier, and to a larger degree, than in other Canadian provinces. Thus, these data would suggest an association between mortality reduction and organization of cancer care, such as implementation of cancer guidelines, community work, and associated funding. No significant financial relationships to disclose.
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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 | 0.002 |
| 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) | 0.003 | 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".