Reducing Uncertainties in Low Dose/Low Dose Rate Health Risks Requires International Networking in Research Implementation and Its Communication to Stakeholders
Bibliographic record
Abstract
The robustness of the international system of radiological protection relies on regularly updating its scientific underpinnings while providing a clear understanding of the assumptions needed to cope with the remaining uncertainties associated to radiation-induced health risks at low dose/low dose rate (i.e. below 100 mSv or below 0.1 mSv/minute). Recent reviews of biological and epidemiological data tend to provide additional support to the assumption that, for low dose, low dose rate exposures, stochastic effects (e.g. cancer risk) follow a dose response with no threshold. However, the adoption of this Linear-Non-Threshold (LNT) model remains controversial because of the large uncertainties persisting about health risks associated with low dose/low dose rate radiation exposures. Current knowledge in low dose or low dose rate radiobiology shows that the mechanisms involved in carcinogenesis are extremely complex. The integration of biological evidence on radiation-induced cancers with epidemiological results offers a promising mechanism-based approach for a less uncertain inference from high doses/dose rates to low doses/dose rates. This approach also applies to non-cancer effects (e.g. circulatory diseases, cognitive effects, lens opacities), where interpretation of epidemiological and animal studies could be reconciled through the development of Adverse Outcome Pathways (AOP), adopting the strategy used for chemical toxicity and its regulation. Given the importance placed on such research by public funding bodies and the broad amount of research being conducted and continued in this area across the globe, while noting national and regional ongoing efforts to effectively collaborate and co-ordinate research, ongoing and planned work merits consideration under an international meta-coordination. One way to reduce uncertainties in low dose/low dose rate health risks is to advance related research strategically, ensuring better use of key results in policy making and improving the way research findings and policies are communicated to stakeholders. To address these issues, the High-Level Group on Low-Dose Research (HLG-LDR) operating under the auspices of the Nuclear Energy Agencies (NEA’s) Committee on Radiological Protection and Public Health (CRPPH), aims to facilitate global networking of low dose research funding organisations and research implementing organisations. This initiative will also integrate a policy-oriented communication strategy on risk uncertainties. Finally, the HLG-LDR activities will enhance the impact of research and have implications for radiological protection policy, regulation and implementation, which will contribute to the revision of the International Commission on Radiological Protection system and beyond.
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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 | 0.289 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".