Bringing together scientific disciplines for collaborative undertakings: a vision for advancing the adverse outcome pathway framework
Bibliographic record
Abstract
BACKGROUND: Decades of research to understand the impacts of various types of environmental occupational and medical stressors on human health have produced a vast amount of data across many scientific disciplines. Organizing these data in a meaningful way to support risk assessment has been a significant challenge. To address this and other challenges in modernizing chemical health risk assessment, the Organisation for Economic Cooperation and Development (OECD) formalized the adverse outcome pathway (AOP) framework, an approach to consolidate knowledge into measurable key events (KEs) at various levels of biological organisation causally linked to disease based on the weight of scientific evidence (http://oe.cd/aops). Currently, AOPs have been considered predominantly in chemical safety but are relevant to radiation. In this context, the Nuclear Energy Agency's (NEA's) High-Level Group on Low Dose Research (HLG-LDR) is working to improve research co-ordination, including radiological research with chemical research, identify synergies between the fields and to avoid duplication of efforts and resource investments. To this end, a virtual workshop was held on 7 and 8 October 2020 with experts from the OECD AOP Programme together with the radiation and chemical research/regulation communities. The workshop was a coordinated effort of Health Canada, the Electric Power Research Institute (EPRI), and the Nuclear Energy Agency (NEA). The AOP approach was discussed including key issues to fully embrace its value and catalyze implementation in areas of radiation risk assessment. CONCLUSIONS: A joint chemical and radiological expert group was proposed as a means to encourage cooperation between risk assessors and an initial vision was discussed on a path forward. A global survey was suggested as a way to identify priority health outcomes of regulatory interest for AOP development. Multidisciplinary teams are needed to address the challenge of producing the appropriate data for risk assessments. Data management and machine learning tools were highlighted as a way to progress from weight of evidence to computational causal inference.
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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 | 0.351 | 0.168 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.021 | 0.096 |
| Scholarly communication | 0.060 | 0.071 |
| Open science | 0.018 | 0.079 |
| Research integrity | 0.042 | 0.031 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".