Income and Education Inequalities in Brain and Central Nervous System Cancer Incidence in Canada: Trends over Two Decades
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The socioeconomic gradient of brain and central nervous system (CNS) cancer incidence in Canada is poorly understood. This study aimed to measure socioeconomic inequalities in brain and CNS cancer incidence in Canada from 1992 to 2010. Using a unique census division level dataset (n = 280) pooled from the Canadian Cancer Registry (CCR), the Canadian Census of Population and the National Household Survey, we measured brain and CNS cancer incidence in Canada. The age-adjusted concentration index (C) was used to measure income- and education-related inequalities in brain and CNS cancers in Canada, and for men and women, separately. Time trend analyses were conducted to examine the changes in socioeconomic inequalities in brain and CNS cancers in Canada over time. The results indicated that the crude brain and CNS cancer incidence increased from 7.29 to 8.17 per 100,000 (annual percentage change: 0.70) over the study period. The age-adjusted C results suggested that the brain and CNS cancer incidence was not generally significantly different for census division of different income and educational levels. There was insufficient evidence to support changes in income and education-related inequalities over time. Since the incidence of brain and CNS cancers in Canada showed no significant association with socioeconomic status, future cancer control programs should focus on other risk factors for this cancer subset.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it