Association of arsenic exposure and cognitive impairment: A population-based cross-sectional study in China
Bibliographic record
Abstract
BACKGROUND: The influence of chronic arsenic exposure on cognitive impairment has been explored broadly by previous studies. However, most of them focused mainly on children rather than adults. In addition, in China, studies in this field are not sufficient. To illustrate how long-term arsenic exposure affects cognitive function, we designed a cross-sectional study involving 1556 adults. METHODS: All of them came from three locations around the Realgar Plant. The cognitive function of the participants was evaluated using a Chinese version of the Mini-mental state Examination (MMSE). The participants' internal arsenic exposure status (hair arsenic concentrations) and the external arsenic exposure status (the distance between the participants' location of residence and the Realgar Plant) were measured. RESULTS: Our research revealed that both of hair arsenic concentrations and the prevalence of arsenicosis, two important indexes, were significantly higher in the cognitive-impaired (CI) group than in the cognitive-normal (CN) group (P < 0.05). In addition, distance from the Realgar Plant was positively correlated with the MMSE scores and was negatively correlated with the prevalence of cognitive impairment. Moreover, our results demonstrated that there was a negative correlation between hair arsenic concentrations and MMSE scores. We conducted a two-level Logistic regression analysis and further confirmed that even after adjusting for potential confounding variables, arsenicosis retained a risk factor for cognitive impairment (odds ratio (OR) = 1.84, P < 0.05). CONCLUSIONS: Our results indicated that chronic arsenic exposure could impair adults' cognitive function in a dose-dependent manner. Additionally, arsenicosis could be an independent risk factor for cognitive impairment.
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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 | 0.002 |
| 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".