Translational stroke research is a challenging task that needs long term team work of the stroke research community. Highly reproducible stroke models with excellent outcome consistence are essential for obtaining useful data from preclinical stroke trials as well as for improving inter-lab comparability. However, our review of literature shows that the infarct variation coefficient of commonly performed stroke models ranges from 5% to 200%. An overall improvement of the commonly used stroke models will further improve the quality for experimental stroke research as well as inter-lab comparability. Many factors play a significant role in causing outcome variation; however, they have not yet been adequately addressed in the Stroke Therapy Academic Industry Roundtable (STAIR) recommendations and the Good Laboratory Practice (GLP). These critical factors include selection of anesthetics, maintenance of animal physiological environment, stroke outcome observation, and model specific factors that affect success rate and variation. The authors have reviewed these major factors that have been reported to influence stroke model outcome, herewith, provide the first edition of stroke model guidelines so to initiate active discussion on this topic. We hope to reach a general agreement among stroke researchers in the near future with its successive updated versions.

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