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Adverse Outcome Pathways Applied to Space Radiation Research

2024· preprint· en· W4405320488 on OpenAlexafffund
Vinita Chauhan, Veronica S. Grybas, Devyn Hoopfer, Casey Higginson, Elizabeth A. Ainsbury, Omid Azimzadeh, Afshin Beheshti, Steve R. Blattnig, Marjan Boerma, Sylvain V. Costes, Stephen B. Doty, Christelle Adam‐Guillermin, Nobuyuki Hamada, Patricia Hinton, Janice L. Huff, Robert J. Reynolds, Ruth C. Wilkins, Scott J. Wood, Carole L. Yauk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsCanadian Forces CollegeUniversity of OttawaHealth Canada
FundersCanadian Space AgencyHealth CanadaCanada Research ChairsNational Aeronautics and Space AdministrationImperial College LondonNational Institute for Health Research Health Protection Research UnitNational Institute for Health and Care ResearchUniversity of Ottawa
KeywordsAdverse Outcome PathwayOutcome (game theory)Space (punctuation)Space radiationAdverse effectPsychologyMedicineComputer sciencePhysicsEconomicsInternal medicineBiologyAstronomyComputational biologyMathematical economics

Abstract

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IntroductionThe Organisation for Economic Co-operation and Development (OECD) Adverse Outcome Pathway (AOP) framework is used to organize scientific knowledge in toxicology into linear sequences of causally related events that lead to adverse toxicological endpoints (Ankley et al., 2010; OECD, 2018). AOPs describe the critical interactions of a chemical or non-chemical stressor within a biological system. AOPs begins with a molecular initiating event (MIE) that leads to intermediate key events (KEs) and culminate in an adverse outcome (AO). KEs are connected by key event relationships (KERs) for which causality is evaluated using the modified Bradford-Hill criteria (Becker et al., 2015). These criteria include biological plausibility, evidence for the essentiality of KEs, and empirical evidence in the form of dose-, temporal- and incidence-concordance. The strength of these directional and causal relationships is evaluated through a weight of evidence analysis for each KER (Villeneuve et al., 2014). AOPs are purposefully simplified, describing KEs that can be routinely measured and are essential to pathway progression, to facilitate regulatory utility and test strategy development (Ankley et al., 2010). AOPs are developed in a linear manner; however, shared KEs lead to networks of AOPs. Through these networks, multiple MIEs can converge to lead to the same AOs. In addition, multiple different types of stressors may interact with the same MIE to progress AOPs. Thus, although simple in concept, the AOP network is the fundamental unit of application for risk assessment and can broadly reflect complex, multi-stressor interactions and outcomes. AOPs have primarily been used to describe the impacts of chemicals on human and ecological outcomes. However, there is growing interest in applying AOPs in the radiation field (NCRP 2020, Chauhan et al., 2019; Chauhan et al., 2024). A case example AOP to lung cancer that is relevant to stressors such as radon inhalation has been endorsed by the Nuclear Energy Agency (NEA) and the Working Group of the National Coordinators of the Test Guidelines Programme (WNT) and Working Party on Hazard Assessment (WPHA) of the OECD (https://aopwiki.org/aops/272). Furthermore, since June 2021, a Radiation/Chemical AOP Joint Topical Group under the auspices of the NEA High Level Group on Low Dose Research has been working to promote and integrate AOPs into radiation research and risk assessments (Chauhan, Beaton et al., 2022a; Chauhan, Hamada et al., 2022b; Chauhan, Beaton et al., 2024). As part of these efforts, AOPs are being developed to multiple AOs including those relevant to space exploration (Kozbenko et al., 2024; Carrothers et al., 2024; Sleiman et al., 2024; Sandhu et al., 2024). Herein we describe four AOPs (AOP#478; AOP#483; AOP#470; AOP#482) that form a network leading to non-cancer AOs of relevance to space travel.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.053
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0120.011
Science and technology studies0.0020.006
Scholarly communication0.0080.005
Open science0.0030.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0450.005

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.

Opus teacher head0.130
GPT teacher head0.420
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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