Cardiovascular disease and arsenic exposure in Inner Mongolia, China: a case control study
Bibliographic record
Abstract
BACKGROUND: Millions of people are at risk from the adverse effects of arsenic exposure through drinking water. Increasingly, non-cancer effects such as cardiovascular disease have been associated with drinking water arsenic exposures. However, most studies have been conducted in highly exposed populations and lacked individual measurements. OBJECTIVE: To evaluate the association between cardiovascular disease and well-water arsenic exposure. METHODS: We conducted a hospital based case control study in Inner Mongolia, China. Cases and controls were prospectively identified and enrolled from a large hospital in the Hangjin Hou area. Cases were patients diagnosed with cardiovascular disease and controls were patients free from cardiovascular disease, admitted for conditions unrelated to arsenic exposure. Water from the primary water source and toenail samples were collected from each subject and tested for inorganic arsenic. RESULTS: Arsenic exposures were moderate with mean and median arsenic exposures of 8.9 μg/L and 13.1 μg/L, respectively. A total of 298 cases and 275 controls were enrolled. The adjusted odds ratio (AOR) and corresponding 95% confidence interval (95% CI) for a 10 μg/L increase in water arsenic were 1.19 (95% CI: 1.03, 1.38). Compared to exposures less than 10 μg/L, the AOR for water arsenic exposures above 40 μg/L was 4.05 (95% CI: 1.1-14.99, p = 0.04). Nail arsenic above 1.38 μg/g was also associated with an increased risk of cardiovascular disease. CONCLUSIONS: By using standardized case definitions and collecting individual measurements of arsenic, this study addressed several limitations of previous studies. The results provide further evidence of the association between cardiovascular disease and arsenic at moderate exposures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".