Disinfection by products and bladder cancer: a pooled analysis
Bibliographic record
Abstract
BACKGROUND: \n\nExposure to disinfection byproducts in drinking water has been associated with an increased risk of bladder cancer. We pooled the primary data from 6 case-control studies of bladder cancer that used trihalomethanes as a marker of disinfection byproducts.\n\nMETHODS: \n\nTwo studies were included from the United States and one each from Canada, France, Italy, and Finland. Inclusion criteria were availability of detailed data on trihalomethane exposure and individual water consumption. The analysis included 2806 cases and 5254 controls, all of whom had measures of known exposure for at least 70% of the exposure window of 40 years before the interview. Cumulative exposure to trihalomethanes was estimated by combining individual year-by-year average trihalomethane level and daily tap water consumption.\n\nRESULTS: \n\nThere was an adjusted odds ratio (OR) of 1.24 in men exposed to an average of more than 1 microg/L (ppb) trihalomethanes compared with those who had lower or no exposure (95% confidence interval [CI] = 1.09-1.41). Estimated relative risks increased with increasing exposure, with an OR of 1.44 (1.20-1.73) for exposure higher than 50 microg/L (ppb). Similar results were found with other indices of trihalomethane exposure. Among women, trihalomethane exposure was not associated with bladder cancer risk (0.95; 0.76-1.20).\n\nCONCLUSIONS: \n\nThese findings strengthen the hypothesis that the risk of bladder cancer is increased with long-term exposure to disinfection byproducts at levels currently observed in many industrialized countries.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".