Overview: Using Mode of Action and Life Stage Information to Evaluate the Human Relevance of Animal Toxicity Data
Bibliographic record
Abstract
A complete mode of action human relevance analysis--as distinct from mode of action (MOA) analysis alone--depends on robust information on the animal MOA, as well as systematic comparison of the animal data with corresponding information from humans. In November 2003, the International Life Sciences Institute's Risk Science Institute (ILSI RSI) published a 2-year study using animal and human MOA information to generate a four-part Human Relevance Framework (HRF) for systematic and transparent analysis of MOA data and information. Based mainly on non-DNA-reactive carcinogens, the HRF features a "concordance" analysis of MOA information from both animal and human sources, with a focus on determining the appropriate role for each MOA data set in human risk assessment. With MOA information increasingly available for risk assessment purposes, this article illustrates the further applicability of the HRF for reproductive, developmental, neurologic, and renal endpoints, as well as cancer. Based on qualitative and quantitative MOA considerations, the MOA/human relevance analysis also contributes to identifying data needs and issues essential for the dose-response and exposure assessment steps in the overall 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".