DRINKING WATER NITRATES AND CANCER: REVIEW OF EPIDEMIOLOGIC STUDIES
Bibliographic record
Abstract
ISEE-256 Abstract: Several reviews of epidemiologic studies on cancer and drinking water nitrates have been conducted but they focused mainly on ecological studies. We present a systematic review on this topic limited to epidemiologic studies with individual data. Our objective was to answer to the question asked by risk assessors: is there a link in epidemiologic studies between ingestion of nitrates in water and some cancer sites? Our selection criteria were: epidemiologic study on adults cancer with individual data on exposure and confounders plus exposure data on drinking water nitrates with either consumption and/or concentration of water nitrates data. After a systematic search from various databases, we identified 9 original papers published between January 1970 and September 2003 in accordance to our selection criteria. Data on methodology and results were extracted by the three authors independently. Eight studies were case-control and one a cohort. Few cancer sites were frequently studied: four studies on gastric cancer, three on Non Hodgkin lymphoma, and two on bladder cancer. Assessment of their quality revealed that only 5 had good quality. The major methodological problems were: exposure assessment and confounders’ control. Concentrations of nitrates were generally low. Classification of exposure varied between each study and heterogeneity of results was important for all the three sites. No metaRR could be estimated. We conclude that no link is established between drinking water nitrates exposure and cancer but that few studies with individual data were done and very few were of good quality. Recommendations for further study will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".