ReviewEnvironmental health perspectives2015
A Unified Probabilistic Framework for Dose-Response Assessment of Human Health Effects.
Review in Environmental health perspectives, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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.
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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.
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Who cites it
49 citing papers in PubMed, 98 citations in OpenAlex.
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- Comparative review of human health reference values of the United States.Toxicological sciences : an official journal of the Society of Toxicology · 2025Review
- Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data.Journal of chemical information and modeling · 2025Article
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- Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.Environmental science & technology · 2024Article
- Article
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- Use of human predictive patch test (HPPT) data for the classification of skin sensitization hazard and potency.Archives of toxicology · 2024Review
- Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol.Environment international · 2023Article
- Quantitative Integration of Mode of Action Information in Dose-Response Modeling and POD Estimation for Nonmutagenic Carcinogens: A Case Study of TCDD.Environmental health perspectives · 2023Article
- A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Basic concepts of mixture toxicity and relevance for risk evaluation and regulation.Archives of toxicology · 2023Article
- Bayesian-Based Probabilistic Risk Assessment of Fipronil in Food: A Case Study in Taiwan.Toxics · 2023Article
- Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment.Environment international · 2023Article
- A value of information framework for assessing the trade-offs associated with uncertainty, duration, and cost of chemical toxicity testing.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals.Environmental health perspectives · 2023Article
- Systematic evidence map (SEM) template: Report format and methods used for the US EPA Integrated Risk Information System (IRIS) program, Provisional Peer Reviewed Toxicity Value (PPRTV) program, and other "fit for purpose" literature-based human health analyses.Environment international · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhen chemical health hazards have been identified, probabilistic dose-response assessment ("hazard characterization") quantifies uncertainty and/or variability in toxicity as a function of human exposure. Existing probabilistic approaches differ for different types of endpoints or modes-of-action, lacking a unifying framework.
objectivesWe developed a unified framework for probabilistic dose-response assessment.
methodsWe established a framework based on four principles: a) individual and population dose responses are distinct; b) dose-response relationships for all (including quantal) endpoints can be recast as relating to an underlying continuous measure of response at the individual level; c) for effects relevant to humans, "effect metrics" can be specified to define "toxicologically equivalent" sizes for this underlying individual response; and d) dose-response assessment requires making adjustments and accounting for uncertainty and variability. We then derived a step-by-step probabilistic approach for dose-response assessment of animal toxicology data similar to how nonprobabilistic reference doses are derived, illustrating the approach with example non-cancer and cancer datasets.
resultsProbabilistically derived exposure limits are based on estimating a "target human dose" (HDMI), which requires risk management-informed choices for the magnitude (M) of individual effect being protected against, the remaining incidence (I) of individuals with effects ≥ M in the population, and the percent confidence. In the example datasets, probabilistically derived 90% confidence intervals for HDMI values span a 40- to 60-fold range, where I = 1% of the population experiences ≥ M = 1%-10% effect sizes.
conclusionsAlthough some implementation challenges remain, this unified probabilistic framework can provide substantially more complete and transparent characterization of chemical hazards and support better-informed risk management decisions.
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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.