More outcomes than trials: a call for consistent data collection across stroke rehabilitation trials

All the more reason to have a Great stroke association   
Everyone must be incompetent, we should ask for their performance evaluations
http://onlinelibrary.wiley.com/doi/10.1111/j.1747-4949.2012.00973.x/abstract?deniedAccessCustomisedMessage=&userIsAuthenticated=false
Stroke survivors experience complex combinations of impairments, activity limitations, and participation restrictions. The essential components of stroke rehabilitation remain elusive. Determining efficacy in randomized controlled trials (RCTs) is challenging; there is no commonly agreed primary outcome measure for rehabilitation trials. Clinical guidelines depend on proof of efficacy in RCTs and meta-analyses. However, diverse trial aims, differing methods, inconsistent data collection, and use of multiple assessment tools hinder comparability across trials. Consistent data collection in acute stroke trials has facilitated meta-analyses to inform trial design and clinical practice. With few exceptions, inconsistent data collection has hindered similar progress in stroke rehabilitation research. There is an urgent need for the routine collection of a core dataset of common variables in rehabilitation trials. The European Stroke Organisation Outcomes Working Group, the National Institutes of Neurological Disorders and Stroke Common Data Elements project, and the Collaborative Stroke Audit and Research project have called for consistency in data collection in stroke trials. Standardizing data collection can decrease study start up times, facilitate data sharing, and inform clinical guidelines. Although achieving consensus on which outcome measures to use in stroke rehabilitation trials is a considerable task, perhaps a feasible starting point is to achieve consistency in the collection of data on demography, stroke severity, and stroke onset to inclusion times. Longer term goals could include the development of a consensus process to establish the core dataset. This should be endorsed by researchers, funders, and journal editors in order to facilitate sustainable change.