Population-Based Cancer Survival in Canada and the United States by Socioeconomic Status: Findings from the CONCORD-2 Study.
Bibliographic record
Abstract
Background: Population-based cancer survival provides insight into the effectiveness of health systems to care for all residents with cancer, including those in marginalized groups. Methods: Using CONCORD-2 data, we estimated 5-year net survival among patients diagnosed 2004-2009 with one of 10 common cancers, and children diagnosed with acute lymphoblastic leukemia (ALL), by socioeconomic status (SES) quintile, age (0-14, 15-64, ≥65 years), and country (Canada or United States). Results: In the lowest SES quintile, survival was higher among younger Canadian adults diagnosed with liver (23% vs 15%) and cervical (78% vs 68%) cancers and with leukemia (62% vs 56%), including children diagnosed with ALL (92% vs 86%); and higher among older Americans diagnosed with colon (62% vs 56%), female breast (87% vs 80%), and prostate (97% vs 85%) cancers. In the highest SES quintile, survival was higher among younger Americans diagnosed with stomach cancer (33% vs 27%) and younger Canadians diagnosed with liver cancer (31% vs 23%); and higher among older Americans diagnosed with stomach (27% vs 22%) and prostate (99% vs 92%) cancers. Conclusions: Among younger Canadian cancer patients in the lowest SES group, greater access to health care may have resulted in higher cancer survival, while higher screening prevalence and access to health insurance (Medicare) among older Americans during the period of this study may have resulted in higher survival for some screen-detected cancers. Higher survival in the highest SES group for stomach and liver may relate to treatment differences. Survival differences by age and SES between Canada and the United States may help inform cancer control strategies.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".