Fundamental Causes of Colorectal Cancer Mortality: The Implications of Informational Diffusion
Bibliographic record
Abstract
CONTEXT: Colorectal cancer is a major cause of mortality in the United States, with 52,857 deaths estimated in 2012. To explore further the social inequalities in colorectal cancer mortality, we used fundamental cause theory to consider the role of societal diffusion of information and socioeconomic status. METHODS: We used the number of deaths from colorectal cancer in U.S. counties between 1968 and 2008. Through geographical mapping, we examined disparities in colorectal cancer mortality as a function of socioeconomic status and the rate of diffusion of information. In addition to providing year-specific trends in colorectal cancer mortality rates, we analyzed these data using negative binomial regression. FINDINGS: The impact of socioeconomic status (SES) on colorectal cancer mortality is substantial, and its protective impact increases over time. Equally important is the impact of informational diffusion on colorectal cancer mortality over time. However, while the impact of SES remains significant when concurrently considering the role of diffusion of information, the propensity for faster diffusion moderates its effect on colorectal cancer mortality. CONCLUSIONS: The faster diffusion of information reduces both colorectal cancer mortality and inequalities in colorectal cancer mortality, although it was not sufficient to eliminate SES inequalities. These findings have important long-term implications for policymakers looking to reduce social inequalities in colorectal cancer mortality and other, related, preventable diseases.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".