Chemical carcinogenicity revisited 3: Risk assessment of carcinogenic potential based on the current state of knowledge of carcinogenesis in humans
Bibliographic record
Abstract
Over 50 years, we have learned a great deal about the biology that underpins cancer but our approach to testing chemicals for carcinogenic potential has not kept up. Only a small number of chemicals has been tested in animal-intensive, time consuming, and expensive long-term bioassays in rodents. We now recommend a transition from the bioassay to a decision-tree matrix that can be applied to a broader range of chemicals, with better predictivity, based on the premise that cancer is the consequence of DNA coding errors that arise either directly from mutagenic events or indirectly from sustained cell proliferation. The first step is in silico and in vitro assessment for mutagenic (DNA reactive) activity. If mutagenic, it is assumed to be carcinogenic unless evidence indicates otherwise. If the chemical does not show mutagenic potential, the next step is assessment of potential human exposure compared to the threshold for toxicological concern (TTC). If potential human exposure exceeds the TTC, then testing is done to look for effects associated with the key characteristics that are precursors to the carcinogenic process, such as increased cell proliferation, immunosuppression, or significant estrogenic activity. Protection of human health is achieved by limiting exposures to below NOEALs for these precursor effects. The decision tree matrix is animal-sparing, cost effective, and in step with our growing knowledge of the process of cancer formation.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".