Nitrate in Drinking Water and Bladder Cancer: A Meta-analysis
Bibliographic record
Abstract
This study examined whether exposure to nitrate in drinking water is associated with increased risk for bladder cancer by conducting a comprehensive literature research. A meta-analysis was performed with and without adjustment for confounding factors. Three groups (reference, intermediate and high groups) were established in terms of different nitrate concentrations in each included study. Separate relative risk measures were calculated for intermediate and high groups. Heterogeneity was assessed by using the Q statistics. Publication bias was evaluated by Egger’s and Begg’s test. Quality assessment for studies was performed by using the Newcastle-Ottawa scale. Two cohorts, two case-controls, and one ecological study were included in this study. The adjusted data showed that the combined risk ratios (RRs) were 1.13 (95% CI: 0.81 to 1.57) and 1.27 (95% CI: 0.75 to 2.15) for inter-mediate and high groups respectively. For unadjusted data, the corresponding RRs were 1.18 (95% CI: 0.89 to 1.57) and 1.29 (95% CI: 0.81 to 2.07). Sensitivity test indicated that results were significantly underestimated when Ward’s study was included. No significant publication bias was found. There was heterogeneity among studies. The results suggested that there was no sufficient evidence that nitrate in drinking water is associated with increased risks for bladder cancer.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.059 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".