EBTC Research Fellowship (August 2018)
EBTC has initiated a part-time Research Fellowship in collaboration with Lancaster University The post is being occupied by Paul Whaley, enabling his continued contributing to our work developing a critical appraisal tool for in vitro studies, and allowing Lancaster University and EBTC to collaborate in advancing our mutual interests in systematic mapping methods and the application of machine learning techniques to the automation of systematic reviews.
Evidence Integration Colloquium report published (May 2018)
We are pleased to inform you that the report on the EFSA/EBTC Colloquium on Evidence Integration in risk assessment is now published and is available here. EBTC is and EFSA are continuing the collaboration and are working on the topics for a possible 2019 joint Colloquium, continuing with the topic of evidence integration. If you are interested in getting involved, please contact us here.
Publishing standards in systematic review: Collaboration for Environmental Evidence Conference (April 2018)
Paris, France. EBTC Director Katya Tsaioun presented lessons learned from using systematic review in two projects aimed at comparing performance of the toxicology test methods and EBTC Research Fellow Paul Whaley discussed interventions which could improve the quality of systematic reviews published by environmental health and toxicology journals in two presentations at the second conference of the Collaboration for Environmental Evidence.
EBTC session at Society of Toxicology Annual Conference (March 2018)
San Antonio, Texas. EBTC's Daniele Wikoff has chaired an Informational Session at SOT 2018, titled "Moving beyond theory to the use of systematic review to support regulatory decision making for evidence-based risk assessment". The session featured Kristina Thayer (US EPA IRIS program), Elisa Aiassa (EFSA), Heather Reddick and Jessica Myers (Texas Commission on Environmental Quality), and Katya Tsaioun (EBTC), sharing their practical experience of using SR methodologies for risk assessment.
EBTC comments in support of IRIS program (February 2018)
EBTC submitted written comments to a public workshop organised by the NAS Committee to Review Advances Made to the IRIS Process. We emphasised the value and benefit to us from our collaboration with the new IRIS leadership at EPA (Kris Thayer, Director of the EPA IRIS program is a member of the EBTC Board of Trustees) and the EPA's role as a leader among US agencies in adopting the systematic review as part of chemical risk assessment, with such work being key to modernising risk assessment and building trust with all stakeholders
EBTC colleagues contribute to new GRADE guidance for interpreting strength of evidence from preclinical animal studies (February 2018)
Although the framework is primarily intended for the field of preclinical animal studies, it is also highly relevant for the field of toxicology, since the two fields share many issues including e.g. how to take into account how well the results from animals translate to humans. A number of researchers involved with EBTC, including Kris Thayer, Holger Schünemann, Emily Sena and Rob de Vries contributed to its development. (See here for the publication.)
Director of US EPA IRIS programme brings top-tier expertise to EBTC (December 2017)
EBTC is pleased to announce that Kristina Thayer, Ph.D. has joined our Board of Trustees. Dr Thayer serves as the Integrated Risk Information System (IRIS) Division Director, located within the US Environmental Protection Agency (EPA) National Center for Environmental Assessment (NCEA).
EBTC sponsors 4th International Symposium on Systematic Review and Meta-Analysis of Laboratory Animal Studies (ISSRMALAS) (September 2017)
This was another excellent conference in this series, with high-caliber speakers and EBTC staff presenting new research findings and sponsoring the poster session. Areas of particular interest in the EBT domain include the rise in interest in systematic mapping techniques as a precursor to systematic reviews, the growing importance of machine learning for dealing with the data volume problem in CRA.