Comprehensive Cancer Survival by Neighborhood-Level Income in Ontario, Canada, 2006-2011.
Bibliographic record
Abstract
BACKGROUND: Cancer survival statistics can provide a means to assess the effectiveness of the cancer care system, including early detection strategies, the quality of clinical care, and disease management. Disparities in cancer survival (for instance, by neighborhood-level income) persist in Ontario, Canada despite the existence of a universal health care system. Lower income has been associated with an increased incidence of cancer and worsened survival. PURPOSE: This project aims to analyze and report on relative survival to provide a mechanism for understanding the level of equity within Ontario's cancer care system. METHODS: Age-standardized relative survival ratios (ARSRs) by cancer type and age group were estimated for 229,934 Ontario adults aged 15-99 years diagnosed between 2006 and 2011 with 1 of 9 cancer types (stomach, colorectal, liver, lung, breast, cervical, ovarian, prostate, and leukemia) using a complete survival analysis. Using the Pohar-Perme estimator, the 1-, 3- and 5-year ARSRs with 95% confidence intervals were calculated by patients' neighborhood-level income quintile. Estimates were age-standardized using the International Cancer Survival Standard weights. RESULTS: Fifty-four relative survival trend curves were developed covering 9 cancers by neighborhood-level income for Ontarians in 5 different age groups and all age groups combined. Disparities in cancer survival were observed between income groups and across age groups and different cancer types in Ontario. For most cancer types and age groups, survival was higher in higher income groups, but this trend was not consistently observed in adolescents and young adults aged 15-44 years. CONCLUSIONS: Disparities in cancer survival persist in Ontario across income groups. Relative survival was significantly higher for higher (Q4 or Q5) compared to lower (Q1 or Q2) neighborhood-level income populations for most cancer types and age groups. Adolescents and young adults with cancer are a small and unique group of patients in terms of the biology of their cancers and their cancer journey, thereby making the patterns of survival disparities observed in this age group more complicated to interpret. Further examination of factors contributing to these disparities is crucial to eliminate survival disparities, reduce premature deaths, and improve cancer survival in Ontario.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".