Variance in disease risk: rural populations and genetic diversity
Bibliographic record
Abstract
Over 19% of the US population resides in rural areas, where studies of disease risk and disease outcomes are difficult to assess due to smaller populations and lower incidence. While some studies suggest rural disparities for different chronic diseases, the data are inconsistent across geography and definitions of rurality. We reviewed the literature to examine if local variations in population genomic diversity may plausibly explain inconsistencies in estimating disease risk. Many rural communities were founded over 150 years ago by small groups of ethnically and ancestrally similar families. These have since endured relative geographical isolation, similar to groups in other industrialized nations, perhaps resulting in founder effects impacting local disease susceptibility. Studies in Europe and Asia have found that observably different phenotypes may appear in isolated communities within 100 years, and that genomic variation can significantly vary over small geographical scales. Epidemiological studies utilizing common "rural" definitions may miss significant disease differences due to assumptions of risk homogeneity and misinterpretation of administrative definitions of rurality. Local genomic heterogeneity should be an important aspect of chronic disease epidemiology in rural areas, and it is important to consider for designing studies and interpreting results, enabling a better understanding of the heritable components of complex 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".