Is there a role for the adverse outcome pathway framework to support radiation protection?
Bibliographic record
Abstract
PURPOSE: In 2012, the Organization for Economic Cooperation and Development (OECD) formally launched the Adverse Outcome Pathway (AOP) Programme. The AOP framework has the potential for predictive utility in identifying early biological endpoints linked to adverse effects. It uses the weight of correlative evidence to identify a minimal set of measurable key events that link molecular initiating events to an adverse outcome. AOPs have the capability to identify knowledge gaps and priority areas for future research based on relevance to an adverse outcome. In addition, AOPs can identify pathways that are common among multiple stressors, thereby allowing for the possibility of refined risk assessments based on co-exposure considerations. The AOP framework is increasingly being used in chemical and ecological risk assessment; however, its use in the development of radiation-specific pathways has yet to be fully explored. To bring awareness of the AOP framework to the Canadian radiation community, a workshop was held in Canada in June 2018 that brought together radiation experts from Health Canada, the Canadian Nuclear Laboratories, and the Canadian Nuclear Safety Commission. METHODS: The purpose of the workshop was to share knowledge on the AOP framework, specifically (1) to introduce the concept of the AOP framework and its possible utility to Canadian radiation experts; (2) to provide examples on how it has advanced risk assessment; (3) to discuss an illustrative example specific to ionizing radiation; and lastly (4) to identify the broad benefits and challenges of the AOP framework to the radiation community. RESULTS: The participants showed interest in the framework, case examples were described and areas of challenge were identified. Herein, we summarize the outcomes of the workshop. CONCLUSIONS: Overall, participants agreed that by building AOPs in the radiation field, a network of data-sharing initiatives will enhance our interpretation of existing knowledge where current scientific evidence is minimal. They would provide new avenues to understand effects at low-dose and dose-rates and help to quantify the combined effect of multiple stressors on shared mechanistic pathways.
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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 | 0.141 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 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".