Literature DB >> 17356848

Bias in toxicology.

Birgitte Wandall1, Sven Ove Hansson, Christina Rudén.   

Abstract

The potential for bias, i.e., influences that cause results to deviate systematically from the truth is substantial both in toxicological research and in the performance of standardized toxicological testing. In this contribution, major potential sources of bias in toxicological research and testing are identified. Due to the lack of empirical studies of bias in toxicology, very little is known about its prevalence and impact. Areas to consider for such studies are pointed out, and it is suggested that such investigations should be given priority.

Mesh:

Year:  2007        PMID: 17356848     DOI: 10.1007/s00204-007-0194-5

Source DB:  PubMed          Journal:  Arch Toxicol        ISSN: 0340-5761            Impact factor:   5.153


  7 in total

1.  Toxicology for the twenty-first century.

Authors:  Thomas Hartung
Journal:  Nature       Date:  2009-07-09       Impact factor: 49.962

2.  Evidence of citation bias in the pesticide ecotoxicology literature.

Authors:  M L Hanson; L E Deeth; R S Prosser
Journal:  Ecotoxicology       Date:  2018-03-02       Impact factor: 2.823

Review 3.  Childhood Lead Exposure and Adult Neurodegenerative Disease.

Authors:  Aaron Reuben
Journal:  J Alzheimers Dis       Date:  2018       Impact factor: 4.472

4.  Mapping Chemical Respiratory Sensitization: How Useful Are Our Current Computational Tools?

Authors:  Emily Golden; Mikhail Maertens; Thomas Hartung; Alexandra Maertens
Journal:  Chem Res Toxicol       Date:  2020-12-15       Impact factor: 3.739

5.  Qualichem in vivo: a tool for assessing the quality of in vivo studies and its application for bisphenol A.

Authors:  Laura Maxim; Jeroen P van der Sluijs
Journal:  PLoS One       Date:  2014-01-29       Impact factor: 3.240

Review 6.  A primer on systematic reviews in toxicology.

Authors:  Sebastian Hoffmann; Rob B M de Vries; Martin L Stephens; Nancy B Beck; Hubert A A M Dirven; John R Fowle; Julie E Goodman; Thomas Hartung; Ian Kimber; Manoj M Lalu; Kristina Thayer; Paul Whaley; Daniele Wikoff; Katya Tsaioun
Journal:  Arch Toxicol       Date:  2017-05-13       Impact factor: 5.153

Review 7.  On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals.

Authors:  Dayne L Filer; Kate Hoffman; Robert M Sargis; Leonardo Trasande; Christopher D Kassotis
Journal:  Environ Health Perspect       Date:  2022-05-09       Impact factor: 11.035

  7 in total

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