The Human Relevance of Information on Carcinogenic Modes of Action: Overview
Bibliographic record
Abstract
Risk assessment policies and practice place increasing reliance on mode of action (MOA) data to inform conclusions about the human relevance of animal tumors. In June 2001, the Risk Science Institute of the International Life Sciences Institute formed a workgroup to study this issue. The workgroup divided into two subgroups, one developing and testing a "framework" for MOA relevance analysis and the other conducting an in-depth analysis of peroxisome proliferation-activated receptor (PPAR)alpha activation as the MOA for some animal carcinogens. This special issue of Critical Reviews in Toxicology presents the scientific reports emerging from this activity. These reports serve several purposes. For risk assessors in and out of government, they offer a new human relevance framework (HRF) that complements and extends existing guidance from other organizations. Regarding the specific MOA for peroxisome proliferating chemicals, these reports offer a state-of-the-science review of this important MOA and its role in tumorigenesis in three different tissues (liver, testis, and pancreas). The case studies in these reports present models for using MOA information to evaluate the hazard potential for humans. The cases also illustrate the substantial impact of a complete human relevance analysis, as distinct from an animal MOA analysis alone, on the nature and scope of risk assessment.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".