Cancer survival in Ontario, 1986-2003: evidence of equitable advances across most diverse urban and rural places.
Bibliographic record
Abstract
OBJECTIVES: This study examined whether place and socio-economic status had differential effects on the survival of women diagnosed with breast cancer in Ontario during the 1980s and the 1990s. METHODS: The Ontario Cancer Registry provided 29,934 primary malignant breast cancer cases. Successive historical cohorts (1986-1988 and 1995-1997) were, respectively, followed until 1994 and 2003. Diverse places were compared: the greater metropolitan Toronto area, other cities, ranging in size from 50,000 to a million people, smaller towns and villages, and rural and remote areas. Socio-economic data for each woman's residence at the time of diagnosis were taken from population censuses. RESULTS: Very small cities (6%) with populations between 50,000 and 100,000 were the only places where breast cancer survival had advanced less compared to the province as a whole. Income gradients began to appear, however, in larger cities. Urban residents in the lowest income areas were significantly disadvantaged compared to the highest income areas during the 1990s, but not during the 1980s. CONCLUSION: This historical analysis of breast cancer survival evidenced remarkably equitable advances across nearly all of Ontario's diverse places. The most likely explanation for such substantial equity seems to be Canada's universally accessible, single-payer, health care system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".