Opportunities to integrate new approaches in genetic toxicology: An ILSI-HESI workshop report
Bibliographic record
Abstract
Genetic toxicity tests currently used to identify and characterize potential human mutagens and carcinogens rely on measurements of primary DNA damage, gene mutation, and chromosome damage in vitro and in rodents. The International Life Sciences Institute Health and Environmental Sciences Institute (ILSI-HESI) Committee on the Relevance and Follow-up of Positive Results in In Vitro Genetic Toxicity Testing held an April 2012 Workshop in Washington, DC, to consider the impact of new understanding of biology and new technologies on the identification and characterization of genotoxic substances, and to identify new approaches to inform more accurate human risk assessment for genetic and carcinogenic effects. Workshop organizers and speakers were from industry, academe, and government. The Workshop focused on biological effects and technologies that would potentially yield the most useful information for evaluating human risk of genetic damage. Also addressed was the impact that improved understanding of biology and availability of new techniques might have on genetic toxicology practices. Workshop topics included (1) alternative experimental models to improve genetic toxicity testing, (2) Biomarkers of epigenetic changes and their applicability to genetic toxicology, and (3) new technologies and approaches. The ability of these new tests and technologies to be developed into tests to identify and characterize genotoxic agents; to serve as a bridge between in vitro and in vivo rodent, or preferably human, data; or to be used to provide dose response information for quantitative risk assessment was also addressed. A summary of the workshop and links to the scientific presentations are provided.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".