A Prospective Study of Arsenic Exposure From Drinking Water and Incidence of Skin Lesions in Bangladesh
Bibliographic record
Abstract
Elevated concentrations of arsenic in groundwater pose a public health threat to millions of people worldwide. The authors aimed to evaluate the association between arsenic exposure and skin lesion incidence among participants in the Health Effects of Arsenic Longitudinal Study (HEALS). The analyses used data on 10,182 adults free of skin lesions at baseline through the third biennial follow-up of the cohort (2000-2009). Discrete-time hazard regression models were used to estimate hazard ratios and 95% confidence intervals for incident skin lesions. Multivariate-adjusted hazard ratios for incident skin lesions comparing 10.1-50.0, 50.1-100.0, 100.1-200.0, and ≥200.1 μg/L with ≤10.0 μg/L of well water arsenic exposure were 1.17 (95% confidence interval (CI): 0.92, 1.49), 1.69 (95% CI: 1.33, 2.14), 1.97 (95% CI: 1.58, 2.46), and 2.98 (95% CI: 2.40, 3.71), respectively (P(trend) = 0.0001). Results were similar for the other measures of arsenic exposure, and the increased risks remained unchanged with changes in exposure in recent years. Dose-dependent associations were more pronounced in females, but the incidence of skin lesions was greater in males and older individuals. Chronic arsenic exposure from drinking water was associated with increased incidence of skin lesions, even at low levels of arsenic exposure (<100 μg/L).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".