Socioeconomic inequalities in kidney and renal pelvis cancer mortality in Canada: Trends over three decades
Bibliographic record
Abstract
BACKGROUND: Kidney and renal pelvis cancer (KCa) presents significant health challenges that require investigation. This study measured and examined trends in socioeconomic inequalities in the mortality of KCa in Canada over the period 1990-2019. METHODS: We constructed a census division level dataset pooled from the Canadian Vital Death Statistics Database (CVSD), the Canadian Census of the Population (CCP), and the National Household Survey (NHS) to measure income and education inequalities in the mortality rate of KCa in Canada over the study period. The age-standardized Concentration index (C), which measures inequality across all socioeconomic groups, was used to quantify income and education inequalities in the mortality of KCa in Canada. Trend analyses evaluated changes in these inequalities over time. RESULTS: The average crude KCa mortality rates were found to be 5.97 and 3.40 per 100,000 for the male and female populations, respectively. The crude KCa mortality consistently increased over time in eastern but not western Canada. Statistically negative values of the age-standardized C index showed higher KCa mortality in the lower-income and less-educated population, particularly among females, with no changes observed over the 30-year study period. CONCLUSION: The higher KCa mortality in socioeconomically disadvantaged groups in Canada indicates the continuing need for primary prevention through lowering smoking rates, reducing obesity, and controlling hypertension. Additionally, promoting greater use of abdominal imaging for the incidental early KCa detection can enable more effective treatment and improved survival rates, especially for females of lower socioeconomic status.
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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 | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".