Evaluating the intersection of climate vulnerability and cancer burden in North Carolina
Bibliographic record
Abstract
Climate-related extreme weather events disrupt health-care systems and exacerbate health disparities, particularly affecting individuals diagnosed with cancer. This study explores the intersection of climate vulnerability and cancer burden in North Carolina (NC). Using county-level data from the US Climate Vulnerability Index (CVI) and the NC Department of Health and Human Services, we analyzed cancer incidence and mortality rates from 2017 to 2021. Our findings reveal a robust correlation between CVI percentiles and cancer mortality (r = 0.72). Counties with high area deprivation like Scotland, Robeson, and Halifax had the highest CVI percentiles of 0.68, 0.67, and 0.66, with respective cancer mortality rates of 193, 195, and 196 per 100 000 person-years. Correlations between CVI and cancer incidence were modest (r = 0.22). These results underscore the need for targeted public health interventions to mitigate climate-related health disparities. Future work could focus on exploring specific climate hazards and cancer outcomes to enhance preparedness and resilience in cancer care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".