A prospective cohort study of stroke mortality and arsenic in drinking water in Bangladeshi adults
Bibliographic record
Abstract
BACKGROUND: Arsenic in drinking water causes increased coronary artery disease (CAD) and death from CAD, but its association with stroke is not known. METHODS: Prospective cohort study with arsenic exposure measured in well water at baseline. 61074 men and women aged 18 years or older on January 2003 were enrolled in 2003. The cohort was actively followed for an average of 7 years (421,754 person-years) through December 2010. Based on arsenic concentration the population was categorized in three groups and stroke mortality HR was compared to the referent. The risk of stroke mortality Hazard Ratio (HR) and 95% Confidence Interval was calculated in relation to arsenic exposure was estimated by Cox proportional hazard models with adjustment for potential confounders. RESULTS: A total of 1033 people died from stroke during the follow-up period, accounting for 23% of the total deaths. Multivariable adjusted HRs (95% confidence interval) for stroke for well water arsenic concentrations <10, 10-49, and ≥50 μg/L were 1.0 (reference), 1.20 (0.92 to 1.57), and 1.35 (1.04 to 1.75) respectively (Ptrend=0.00058). For men, multivariable adjusted HRs (95%) for well water arsenic concentrations <10, 10-49, and ≥50 μg/L were 1.0 (reference), 1.12 (0.78 to 1.60), and 1.07 (0.75 to 1.51) respectively (Ptrend=0.45) and for women 1.0 (reference),1.31 (0.87 to 1.98), and 1.72 (1.15 to 2.57) respectively (Ptrend=0.00004). CONCLUSION: The result suggests that arsenic exposure was associated with increased stroke mortality risk in this population, and was more significant in women compared to men.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".