ArticleEvidence-based toxicology2025
An environmental health vocabulary and its semi-automated curation workflow.
Article in Evidence-based toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways.Computational toxicology (Amsterdam, Netherlands) · 2025Article
- Development of the toxicity values database, ToxValDB: A curated resource for experimental and derived human health-relevant toxicity data.Computational toxicology (Amsterdam, Netherlands) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The environmental health vocabulary (EHV) represents manually curated terminologies developed by the US Environmental Protection Agency's (EPA) Chemical Pollutant Assessment Division (CPAD) for standardizing reporting of health effect information. Recognizing that manual data curation is a resource bottleneck, a semi-automated curation workflow was realized. The objectives of this work are to describe the manual creation of the EHV and improve the efficiency of manual data curation by implementing a new semi-automated curation workflow that minimizes manual review using a sequence of computational text analysis and quality assurance/quality control (QA/QC) steps with a high level of accuracy. To facilitate semi-automated curation a sequence of computational text analysis and manual steps were developed. Described are (1) a series of computational text processing steps to normalize and match extracted terms to the EHV, (2) a QA step of the computationally identified matches; (3) a manual review of unmatched terms; and (4) curation of the EHV that includes completion of missing hierarchical data and related metadata. The EHV was manually created to promote data aggregation, integration, accessibility and transparent data exchange across EPA partners by normalizing the data extracted into the EPA Health Assessment Workplace Collaborative (HAWC). The workflow described here removes the manual curation bottleneck by transforming data curation into a streamlined semi-automated process powered by computational text processing steps. This semi-automated curation method offers several advantages to the environmental health community including (but not limited to) efficiency by automating repeating data management tasks, scalability to a large volume of terms and terminology resources, and better integration with other data sets and artificial intelligence (AI) and machine learning (ML) models.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.