Fundamental Causes of Colorectal Cancer Mortality in the United States: Understanding the Importance of Socioeconomic Status in Creating Inequality in Mortality
Bibliographic record
Abstract
OBJECTIVES: We used the fundamental cause hypothesis as a framework for understanding the creation of health disparities in colorectal cancer mortality in the United States from 1968 to 2005. METHODS: We used negative binomial regression to analyze trends in county-level gender-, race-, and age-adjusted colorectal cancer mortality rates among individuals aged 35 years or older. RESULTS: Prior to 1980, there was a stable gradient in colorectal cancer mortality, with people living in counties of higher socioeconomic status (SES) being at greater risk than people living in lower SES counties. Beginning in 1980, this gradient began to narrow and then reversed as people living in higher SES counties experienced greater reductions in colorectal cancer mortality than those in lower SES counties. CONCLUSIONS: Our findings support the fundamental cause hypothesis: once knowledge about prevention and treatment of colorectal cancer became available, social and economic resources became increasingly important in influencing mortality rates.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".