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Record W4411329725 · doi:10.1016/j.envint.2025.109601

Guidance on minimum information requirements (MIR) from designing to reporting human biomonitoring (HBM)

2025· article· en· W4411329725 on OpenAlexaff
Maryam Zare Jeddi, Karen S. Galea, Jillian Ashley‐Martin, Julianne Nassif, Tyler Pollock, Devika Poddalgoda, Konstantinos M. Kasiotis, Kyriaki Machera, Holger M. Koch, Marta Esteban, Ming Kei Chung, Kate Jones, Adrian Covaci, Yu Ait Bamai, Mariana F. Fernández, Robert Kaše, Henriqueta Louro, Maria João Silva, Tiina Santonen, Andromachi Katsonouri, Argelia Castaño, Lesliam Quirós-Alcalá, Krystal J. Godri Pollitt, Ana Virgolino, Paul T.J. Scheepers, Lisa Jo Melnyk, Vicente Mustieles, Ana Cañas, Natalie von Goetz, Ovnair Sepai, Emily Bird, Thomas Göen, Silvia Fustinoni, Manosij Ghosh, Hubert Dirven, Jung‐Hwan Kwon, Courtney C. Carignan, Yuki Mizuno, Yuki Ito, Yankai Xia, Shoji F. Nakayama, Konstantinos C. Makris, Patrick J. Parsons, Melissa Gonzales, M. Bader, Mária Dušinská, Aziza Menouni, Radu Corneliu Duca, Kaoutar Chbihi, Samir El Jaâfari, Lode Godderis, An Van Nieuwenhuyse, Asif Qureshi, Imran Ali, João Paulo Teixeira, Alena Bartoňová, Giovanna Tranfo, Karine Audouze, Steven Verpaele, Judy S. LaKind, Hans Mol, Jos Bessems, Barbara Magagna, Maisarah Nasution Waras, Alison Connolly, Marc A. Nascarella, Wonho Yang, Po‐Chin Huang, Jueun Lee, Henri Heussen, Özlem Göksel, Masud Yunesian, Leo W. Y. Yeung, Gustavo Souza, Ana Maria Vekic, Erin N. Haynes, Nancy B. Hopf

Bibliographic record

VenueEnvironment International · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsHealth Canada
FundersCenters for Disease Control and PreventionU.S. Environmental Protection AgencyU.S. Department of Health and Human Services
KeywordsBiomonitoringRisk analysis (engineering)Computer scienceEnvironmental healthEnvironmental scienceManagement scienceEngineeringMedicineBiologyEcology

Abstract

fetched live from OpenAlex

Human biomonitoring (HBM) provides an integrated chemical exposures assessment considering all routes and sources of exposure. The accurate interpretation and comparability of biomarkers of exposure and effect depend on harmonized, quality-assured sampling, processing, and analysis. Currently, the lack of broadly accepted guidance on minimum information required for collecting and reporting HBM data, hinders comparability between studies. Furthermore, it prevents HBM from reaching its full potential as a reliable approach for assessing and managing the risks of human exposure to chemicals. The European Chapter of the International Society of Exposure Science HBM Working Group (ISES Europe HBM working group) has established a global human biomonitoring community network (HBM Global Network) to develop a guidance to define the minimum information to be collected and reported in HBM, called the "Minimum Information Requirements for Human Biomonitoring (MIR-HBM)". This work builds on previous efforts to harmonize HBM worldwide. The MIR-HBM guidance covers all phases of HBM from the design phase to the effective communication of results. By carefully defining MIR for all phases, researchers and health professionals can make their HBM studies and programs are robust, reproducible, and meaningful. Acceptance and implementation of MIR-HBM Guidelines in both the general population and occupational fields would improve the interpretability and regulatory utility of HBM data. While implementation challenges remain-such as varying local capacities, and ethical and legal differences at the national levels, this initiative represents an important step toward harmonizing HBM practice and supports an ongoing dialogue among policymakers, legal experts, and scientists to effectively address these challenges. Leveraging the data and insights from HBM, policymakers can develop more effective strategies to protect public health and ensure safer working environments.

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)0.177
metaresearch head score (Gemma)0.258
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.823
Threshold uncertainty score0.936

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1770.258
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0100.006
Science and technology studies0.0040.006
Scholarly communication0.0100.006
Open science0.0170.009
Research integrity0.0220.013
Insufficient payload (model declined to judge)0.0060.008

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.

Opus teacher head0.036
GPT teacher head0.330
Teacher spread0.295 · 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.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations14
Published2025
Admission routes1
Has abstractyes

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