Do disinfection byproducts in drinking water have an effect on human cancer risk worldwide? A meta‐analysis
Bibliographic record
Abstract
Abstract In the present study, a meta‐analysis was carried out to clarify the association between disinfection byproducts (DBPs) in drinking water and human cancer risk worldwide. Kidney, colorectal, esophagus, urinary bladder, brain, breast, leukemia, lung, and rectum cancers were selected to perform this analysis. According to preferred reporting items for systematic review and meta‐analysis protocol (PRISMA) guidelines, the relevant studies were identified and selection criteria (inclusion and exclusion criteria) were applied. Next, effective subgroups in these studies (gender, type of drinking water source, and type of DBPs) were analyzed. The quality of the studies was evaluated using the Newcastle‐Ottawa Scale. In addition, this overall study included analyses of 16 case–control and 3 cohort studies. The overall odds ratio (OR) with 95% confidence intervals (CI) between DBPs and cancer risk was 1.01 (95% CI, 0.94–1.09). The summary ORs of cancer risk were 1.04 (95% CI, 0.89–1.19) for kidney; 0.98 (95% CI, 0.87–1.09) for colorectal; 1.07 (95% CI, 0.84–1.29) for esophagus; 0.93 (95% CI, 0.80–1.06) for pancreatic; 1.00 (95% CI, 0.83–1.18) for brain; 1.13 (95% CI, 0.99–1.26) for breast; 0.93 (95% CI, 0.72–1.13) for leukemia; and 1.18 (95% CI, 1–1.36) for lung cancers. The results of this meta‐analysis suggested that there is not a significant association between DBPs in water and cancer risk. In addition, subgroup analysis shows a positive association with colorectal and kidney cancer risk in men, as well as colon and breast cancers in females. Studies of both genders have shown a significant association between lung and pancreatic cancers. Moreover, this study finds a significant relationship between cancer rate and consumers of city water and bottled water sources. In analyzing different types of DBPs in water, chlorine and trichloromethane show a significant association in increasing cancer risk.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.059 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".