ArticleReviews on environmental health2017
An open-sourced statistical application for identifying complex toxicological interactions of environmental pollutants.
Article in Reviews on environmental health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- The State-of-the Art of Environmental Toxicogenomics: Challenges and Perspectives of "Omics" Approaches Directed to Toxicant Mixtures.International journal of environmental research and public health · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The rising number of chemicals that humans are exposed to on a daily basis, as well as advances in biomonitoring and detection technologies have highlighted the diversity of individual exposure profiles (complex body burdens). To address this, the toxicological sciences have begun to shift away from examining toxic agents or stressors individually to focusing on more complex models with multiple agents or stressors present. Literature on interactions between chemicals is fairly limited in comparison with dose-response studies on individual toxicants, which is largely due to experimental and statistical challenges. Experimental designs capable of identifying these complex interactions are often avoided or not evaluated to their fullest potential because of the difficulty associated with appropriate analysis as well as logistical factors. To assist with statistical analysis of these types of experiments, an online, open-sourced statistical application was created for investigators to use to analyze and interpret potential toxicant interactions in laboratory experimental data using a full-factorial three-way analysis of variance (ANOVA). This model utilizes backward selection on interaction terms to model main effects and interactions.
Indexed as
Identifiers
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.