Using mode of action information to improve regulatory decision-making: An ECETOC/ILSI RF/HESI workshop overview
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it