Alkylating agents are possible inducers of glioblastoma and other brain tumors
Bibliographic record
Abstract
Epidemiological evidence of an association between exposure to chemical carcinogens and an increased risk for development of glioblastoma (GBM) is limited to weak statistical associations in cohorts of firefighters, farmers, residents exposed to air pollution, and soldiers exposed to toxic chemicals (e.g., military burn pits, oil-well fire smoke). A history of ionizing radiation therapy to the head or neck is associated with an increased risk of GBM. Ionizing radiation induces point mutations, frameshift mutations, double-strand breaks, and chromosomal insertions or deletions. Mutational profiles associated with chemical exposures overlap with the broad mutational patterns seen with ionizing radiation. Data on 16 agents (15 chemicals and radio frequency radiation) that induced tumors in the rodent brain were extracted from 602 Technical Reports on 2-years cancer bioassays found in the National Toxicology Program database. Ten of the 15 chemical agents that induce brain tumors are alkylating agents. Three of the 15 chemical agents have idiosyncratic structures and might be alkylating agents. Only two of the 15 chemical agents are definitively not alkylating agents. The rat model is thought to be of possible relevance to humans suggesting that exposure to alkylating chemicals should be considered in epidemiology studies on GBM and other brain tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".