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Record W3160569813 · doi:10.1016/j.envadv.2021.100061

Envisioning an international validation process for New Approach Methodologies in chemical hazard and risk assessment

2021· article· en· W3160569813 on OpenAlexafffund
Matthieu Mondou, Steve Maguire, Guillaume Pain, Doug Crump, Markus Hecker, Niladri Basu, Gordon M. Hickey

Bibliographic record

VenueEnvironmental Advances · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change CanadaUniversité LavalMcGill University
FundersGenome PrairieMcGill UniversityGénome QuébecSocial Sciences and Humanities Research Council of CanadaUniversity of SaskatchewanGovernment of CanadaMinistère de l'Économie, de la Science et de l'Innovation - QuébecEnvironment and Climate Change CanadaGenome Canada
KeywordsContext (archaeology)StandardizationProcess (computing)Delphi methodKnowledge managementComputer scienceGovernment (linguistics)HarmonizationData sharingHazardBusinessProcess managementData scienceMedicine

Abstract

fetched live from OpenAlex

Despite the global toxicology community discussing New Approach Methodologies (NAMs) for chemical hazard and risk assessment, such as in vitro, in silico, and ‘omics-based approaches, for some 30 years, their formal adoption by regulators remains limited. Previous research suggests that insufficient validation, complexity of interpretation, and lack of standardization are salient obstacles to adoption. In this paper we aim to better understand the policy challenges associated with adopting NAMs in chemical risk assessment; and to identify actions that could facilitate and accelerate their formal adoption internationally. We conducted a Delphi study – a group communication process that solicits expert judgments through iterative questioning and feedback – with panelists from government, industry, and non-governmental organizations in Europe and North America. Expert panelists identified two key activities to facilitate and accelerate the validation of NAMs internationally: 1) the development of common data collection, reporting and sharing procedures; and 2) the improvement of knowledge about new test methods among members of the regulatory community. Both activities suggest the need for a common regulatory science infrastructure, including international regulatory dialogues, large-scale research collaborations, and coordinated innovation in technological tools, the discourse of scientific validation, and regulatory procedures. To build trust across many sites (laboratories, regulatory agencies, contract research organizations, chemical producers, and the public), stakeholders will need to agree on validation requirements for particular uses (content in relation to context) as well as how results are to be communicated (data format), and measured (metrics). There is also a need for a global orchestrator, who can exert leadership and inspire voluntary cooperation of diverse organizations to address shared validation goals, to play a key role.

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)0.669
metaresearch head score (Gemma)0.291
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.331
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6690.291
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0070.005
Science and technology studies0.0100.023
Scholarly communication0.0230.029
Open science0.0070.026
Research integrity0.0120.019
Insufficient payload (model declined to judge)0.0030.001

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 head0.173
GPT teacher head0.515
Teacher spread0.343 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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".

Quick stats

Citations31
Published2021
Admission routes2
Has abstractyes

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