An international comparison of cancer survival: relatively poor areas of Toronto, Ontario and three US metropolitan areas
Bibliographic record
Abstract
BACKGROUND: This study of cancer survival compared adults in Toronto, Ontario and three US metropolitan areas: Seattle, Washington; San Francisco, California; and Hartford, Connecticut. It examined whether socioeconomic status has a differential effect on cancer survival in Canada and the United States. METHODS: The Ontario Cancer Registry and the National Cancer Institute's Surveillance, Epidemiology and End RESULTS: (SEER) programme provided a total of 23,437 and 37,329 population-based primary malignant cancer cases for the Toronto and US samples, respectively (1986-1988, followed until 1994). Census-based measures of socioeconomic status were used to ecologically control absolute income status. RESULTS: Among residents of low-income areas, persons in Toronto experienced a 5 year survival advantage for 13 of 15 cancer sites [minimally one gender significant at 95 per cent confidence interval (CI)]. An aggregate 35 per cent survival advantage among the Canadian cohort was demonstrated (survival rate ratio (SRR) = 1.35, 95 per cent CI= 1.30-1.40), and this effect was even larger among younger patients not yet eligible for Medicare coverage in the United States (SRR = 1.46, 95 per cent CI = 1.40-1.52). CONCLUSION: Systematically replicating a previous Toronto-Detroit comparison, this study's observed consistent pattern of Canadian survival advantage across various cancer sites suggests that their more equitable access to preventive and therapeutic health care services may be responsible for the difference.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".