An Epidemiologic Study of Arsenic-Related Skin Disorders and Skin Cancer and the Consumption of Arsenic-Contaminated Well Waters in Huhhot, Inner Mongolia, China
Bibliographic record
Abstract
Well-use histories were obtained and dermatological examinations were conducted for 3,179 of the 3,228 (98.5%) residents of 3 villages in Inner Mongolia with well water arsenic levels as high as 2,000 ppb (ug/L). Eight persons were found to have skin cancer, 172 had hyperkeratoses, 121 had dyspigmentation, 94 had both hyperkeratoses and dyspigmentation, and, strikingly, none had Blackfoot disease. All 8 subjects with skin cancer also had both hyperkeratoses and dyspigmentation. Arsenic levels were measured for 184 wells and individual well-use histories were obtained. Arsenic exposure histories were summarized as both highest arsenic concentration (highest exposure level for at least 1-year duration) and cumulative arsenic exposure (ppb-years). Sixty-nine percent of the participants had highest arsenic concentrations below 100 ppb; 71% had cumulative arsenic exposures below 2,000 ppb-years. Exposure-response analyses included frequency-weighted, simple linear regression, and most-likely estimate (hockey-stick) models. Skin cancer cases were only found for those with a highest arsenic concentration greater than 150 ppb, and those with exposure less than 150 ppb had a statistically significant deficit. A frequency-weighted model showed a threshold at 150 ppb, and a hockey-stick model showed a threshold at 122 ppb. Considerations of duration, age, latency, and misclassification did not appear to markedly affect the analysis. The non-malignant skin findings showed thresholds of 40–50 ppb in the hockey-stick models. Application of these analytic models to the data from other epidemiological studies of arsenic ingestion and malignant and non-malignant skin disorders can be used to examine patterns of arsenic carcinogenicity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".