Breast cancer survival in Canada and the USA: meta-analytic evidence of a Canadian advantage in low-income areas
Bibliographic record
Abstract
BACKGROUND: This study tested the hypothesis that relatively poor Canadian women with breast cancer have a survival advantage over their counterparts in the USA. METHODS: Seventy-eight independent retrospective cohort (incidence between 1984 and 2000, followed until 2006) outcomes were synthesized. Fixed effects meta-regression models compared women with breast cancer in low-income areas of Canada and the USA. RESULTS: Low-income Canadian women were advantaged on survival [rate ratio (RR) = 1.14; 95% confidence interval (CI) 1.13-1.15] and their advantage was even larger among women <65 years of age who are not yet eligible for Medicare coverage in the USA (RR = 1.21, 95% CI 1.18-1.24). Canadian advantages were also larger for node positive breast cancer, which may present with greater clinical and managerial discretion (RR = 1.40, 95% CI 1.30-1.50), and smaller when Hawaii, the state providing the most Canadian-like access, was the US comparator (RR = 1.12, 95% CI 1.01-1.20). CONCLUSIONS: More inclusive health care insurance coverage in Canada vs the USA, particularly among each country's relatively poor people, seems the most plausible explanation for such Canadian advantages. Provision of health care for all Americans would likely prevent countless early deaths, particularly among the relatively poor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".