Using mode of action information to improve regulatory decision-making: An ECETOC/ILSI RF/HESI workshop overview
Bibliographic record
Abstract
The European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), the International Life Sciences Institute (ILSI) Research Foundation (RF), and the ILSI Health and Environmental Sciences Institute (HESI) hosted a workshop in November 2009 to review current practice in the application of mode of action (MOA) considerations in chemical risk assessment. The aim was to provide a rationale for a more general, but flexible approach and to propose steps to facilitate broader uptake and use of the MOA concept. There was consensus amongst the workshop participants that it will require substantial effort and cooperation from the multiple disciplines involved to embrace a common, consistent, and transparent approach. Setting up a repository of accepted MOAs and associated guidance concerning appropriate data to support specific MOAs for critical effects would facilitate categorization of chemicals and allow predictions of toxicity outcomes by read-across. This should in future contribute to the reduction of toxicity testing in animals. The workshop participants also acknowledged the value and importance of human data and the importance of integrating information from biological pathway analyses into current MOA/human relevance frameworks.
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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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".