Marked Variations in Colon Cancer Epidemiology: Sex-specific and Race/Ethnicity-specific Disparities
Bibliographic record
Abstract
BACKGROUND: Recent studies have reported on the changing epidemiology of colon cancer. Given this cancer's high prevalence and mortality, defining high risk groups will be important to guide improvements in cancer screening programs. METHODS: A retrospective cohort study of a large population-based cancer registry in the United States from 1973-2004 was performed to analyze the race and sex-specific disparities in colon cancer epidemiology. RESULTS: Blacks and females demonstrated the greatest proportions of proximal cancers: the incidence rate of proximal cancers among black males was more than double that of Asian males (25.2 per 100,000/year vs 11.7 per 100,000/year, p < 0.0001) and the rate among black females was twice that of Asian females (21.9 per 100,000/year vs 11.4 per 100,000/year, p < 0.0001). Blacks as a group had the highest rates of advanced cancers: the rate among black males was nearly double that of Hispanic males (17.1 per 100,000/year vs 8.7 per 100,000/year, p < 0.0001) and the rate of advanced cancers among black females was twice that of Hispanic females (12.4 per 100,000/year vs 6.2 per 100,000/year, p < 0.0001). CONCLUSIONS: This study demonstrates marked disparities in the sex-specific and race/ethnicity-specific epidemiology of colon cancer. These differences likely represent unequal access to health care resources and race and sex-specific variations in cancer biology. An individualized approach incorporating these disparities would benefit future research and guidelines for improvements in cancer screening programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".