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Record W4405395533 · doi:10.1016/j.toxlet.2024.12.006

Introducing the OECD guidance document on occupational biomonitoring: A harmonized methodology for deriving occupational biomonitoring levels (OBL)

2024· article· en· W4405395533 on OpenAlexaff
Nancy B. Hopf, Jos Bessems, Tiina Santonen, Susana Viegas, Ludwine Casteleyn, Devika Poddalgoda, Farida Lamkarkach, Thomas Göen, Maryam Zare Jeddi, M Koller, Christophe Rousselle, Kate Jones, Kaspar Schmid, Rex FitzGerald, M. Bader, K. Takaki, Patience Browne, Virpi Väänänen, Radu Corneliu Duca, Robert Pasanen‐Kase

Bibliographic record

VenueToxicology Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsHealth Canada
Fundersnot available
KeywordsBiomonitoringOccupational exposureEnvironmental healthEnvironmental scienceEnvironmental chemistryMedicineChemistry

Abstract

fetched live from OpenAlex

Derivation of occupational biomonitoring levels (OBLs) is needed to effectively utilize biomonitoring for assessing exposures to chemical substances, and consequently, implement risk reduction measures to reduce health risks among workers. OBLs are the appropriate option for chemical substances that can be absorbed through the skin. This methodology for derivation of OBLs has been developed in collaboration with scientific and regulatory experts from more than 40 institutes in 15 countries within the Organization for Economic Cooperation and Development (OECD) framework. This manuscript provides a summary of the guidance on derivation of OBLs destined for scientists, risk assessors, and regulators who are tasked with establishing OBLs for regulatory purposes and implementing occupational biomonitoring programs. The derivation methodology follows a tiered approach based on the strength of evidence and quality of the data that we have labeled level of confidence. The tiered approach serves as a practical framework in occupational health risk assessment and management. We distinguish between four OBL levels depending on the strength of scientific evidence and confidence level: health-based derivation of OBL based on robust epidemiological data showing causal exposure-health effect relationship and Provisional OBL (POBL) based on robust toxicological animal data showing dose-response relationship as well as two assessment values which are not health based: reference levels in the general population (Reference OBL or (ROBL)), and Technical achievable OBL or (TOBL). Four case studies illustrating the derivation methods for OBLs and POBLs are also provided. Using this state-of-the-art approach (OECD guidance document no. 370) will lead to a harmonized derivation of OBLs and subsequently to evidence-based risk management measures. • Deriving Occupational biomonitoring levels (OBLs) for assessing multiple exposures. • Human biomonitoring (HBM) detects substances absorbed via skin, lungs, and gut. • OBL and HBM are required for implementing risk reduction measures for workers. • Within OECD framework, experts developed a tiered approach to derive OBLs. • Harmonized OBLs will enable consistent and global protection of workers’ health.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.424
Teacher spread0.358 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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