A review of epidemiologic studies of triazine herbicides and cancer
Bibliographic record
Abstract
This is an update of a previous review of epidemiological evidence pertaining to the human carcinogenic potential of triazine herbicides. In 36 studies evaluated, atrazine was the most common triazine investigated. In general the studies were limited by lack of in-depth exposure measurements and by small numbers of subjects with potential high exposure and/or with many years of follow-up since first exposure. Non-Hodgkin lymphoma, prostate cancer, and breast cancer were most frequently investigated. Only one to three analytical or ecological studies investigated Hodgkin lymphoma, leukemia, multiple myeloma, soft tissue sarcoma, hairy-cell leukemia, melanoma, and cancers of the ovary, testes, colon, stomach, lung, brain, bladder, buccal cavity, and pharynx. Results of these studies were typically imprecise and did not form an adequate basis for determining if triazine exposure causes any form of cancer. Collectively, the available epidemiology studies do not provide consistent, scientifically convincing evidence of a causal relationship between exposure to atrazine or triazine herbicides and cancer in humans. Based upon the assessment studies, there is no scientific basis for inferring the existence of a causal relationship between triazine exposure and the occurrence of cancer in humans.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".