Establishing a communication and engagement strategy to facilitate the adoption of the adverse outcome pathways in radiation research and regulation
Bibliographic record
Abstract
BACKGROUND: Studies on human health and ecological effects of ionizing radiation are rapidly evolving as innovative technologies arise and the body of scientific knowledge grows. Structuring this information could effectively support the development of decision making tools and health risk models to complement current system of radiation protection. To this end, the adverse outcome pathway (AOP) approach is being explored as a means to consolidate the most relevant research to identify causation between exposure to a chemical or non-chemical stressor and disease or adverse effect progression. This tool is particularly important for low dose and low dose rate radiation exposures because of the latency and uncertainties in the biological responses at these exposure levels. To progress this aspect, it is essential to build a community of developers, facilitators, risk assessors (in the private sector and in government), policy-makers, and regulators who understand the strengths and weaknesses of, and how to appropriately utilize AOPs for consolidating our knowledge on the impact of low dose ionizing radiation. Through co-ordination with the Organisation of Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) High-Level Group on Low-Dose Research (HLG-LDR) and OECD's AOP Programme, initiatives are under way to demonstrate this approach in radiation research and regulation. Among these, a robust communications strategy and stakeholder engagement will be essential. It will help establish best practices for AOPs in institutional project development and aid in dissemination for more efficient and timely uptake and use of AOPs. In this regard, on June 1, 2021, the Radiation and Chemical (Rad/Chem) AOP Joint Topical Group was formed as part of the initiative from the NEA's HLG-LDR. The topical group will work to develop a communication and engagement strategy to define the target audiences, establish the clear messages and identify the delivery and engagement platforms. CONCLUSION: The incorporation of the best science and better decision making should motive the radiation protection community to develop, refine and use AOPs, recognizing that their incorporation into radiation health risk assessments is critical for public health and environmental protection in the 21st century.
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.
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 | 0.387 | 0.336 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.008 | 0.066 |
| Research integrity | 0.024 | 0.028 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".