The RIGHT project (Research Into Global Healthcare Tools, http://www.right.org.uk/) is a collaborative project involving five British universities, funded by the UK's Engineering and Physical Sciences Research Council. Phase 1 of the project is due to be completed in October 2009, and this paper describes one strand of the research, namely identifying some of the issues with involving stakeholders in simulation modeling in healthcare. Not the least of these is actually identifying who the stakeholders are! Other problems identified are equally tricky, as they involve deeply rooted cultural and behavioral attitudes as well as complex organizational relationships. One of the underlying aims of the next phase of RIGHT is to tackle these problems and to develop a methodology for more effective stakeholder engagement with simulation modeling.

Conclusions These findings must be interpreted with caution because any word search is dependent on the use of language, which varies between countries and language groups, and over time. Also, the affiliation field refers only to the first author. With these caveats, this historical analysis supports some anecdotal impressions about occupational epidemiology: Nordic countries, relative to their size, have made a major contribution; historically, papers have come from a small pool of countries; the large volume of papers from the USA is likely to be influential; and the trend of accelerating research output seen in the twentieth century may have stabilised. Objectives To develop a framework for handling missing participant data for continuous outcomes in systematic reviews and assess its impact on risk of bias. Methods We conducted a consultative, iterative process. We considered sources that reflect real observed outcomes in participants followed-up in individual trials included in the systematic review, and developed a range of plausible strategies that would be progressively more stringent in challenging the robustness of the pooled estimates. We applied our approach to two example systematic reviews. Results We used 5 sources of data for imputing the means for participants with missing data: [A] the best mean score among the intervention arms of included trials, [B] the best mean score among the control arms of included trials, [C] the mean score from the control arm of the same trial, [D] the worst mean score among the intervention arms of included trials, [E] the worst mean score among the control arms of included trials. To impute SD, we used the median SD from the control arms of all included trials. Using these sources of data, we developed four progressively more stringent imputation strategies. 181In the first example review, effect estimates were diminished and lost significance as the strategies became more stringent, suggesting the need to rate down confidence in estimates of effect for risk of bias. In the second review, effect estimates maintained statistical significance using even the most stringent strategy, suggesting missing data does not undermine confidence in the results. The differences are due to: [1] the size of the effect and its precision, and [2] the percentage of missing participant data. Conclusions Our approach provides rigorous yet reasonable and relatively simple, quantitative guidance for judging the impact of risk of bias as a result of missing participant data in systematic reviews of continuous outcomes. Objective To assess whether English-speaking reviewers can identify foreign-language articles that are eligible for a systematic review of all treatments for fibromyalgia. Methods Systematic review of AMED, CINAHL, EMBASE, MEDLINE, HealthSTAR, PsycINFO, Papers First, Proceedings First and CENTRAL, from inception of each database to April, 2011, to identify all randomised controlled trials exploring any form of therapy for fibromyalgia. All non-English language articles were...