***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background and Purpose — Several prognostic factors have been identified for outcome after stroke . However , there is a need for empirically derived models that can predict outcome and assist in medical management during rehabilitation . To be useful , these models should take into account early changes in recovery and individual patient characteristics . We present such a model and demonstrate its clinical utility . Methods — Data on functional recovery ( Barthel Index ) at 0 , 2 , 4 , 6 , and 12 months after stroke were collected prospect ively for 299 stroke patients at 2 London hospitals . Multilevel models were used to model recovery trajectories , allowing for day-to-day and between-patient variation . The predictive performance of the model was vali date d with an independent cohort of 710 stroke patients . Results — Urinary incontinence , sex , prestroke disability , and dysarthria affected the level of outcome after stroke ; age , dysphasia , and limb deficit also affected the rate of recovery . Applying this to the validation cohort , the average difference between predicted and observed Barthel Index was −0.4 , with 90 % limits of agreement from −7 to 6 . Predicted Barthel Index lay within 3 points of the observed Barthel Index on 49 % of occasions and improved to 69 % when patients ’ recovery histories were taken into account . Conclusions — The model predicts recovery at various stages of rehabilitation in ways that could improve clinical decision making . Predictions can be altered in light of observed recovery . This model is a potentially useful tool for comparing individual patients with average recovery trajectories . Patients at elevated risk could be identified and interventions initiated Prognosis studies are investigations of future events or the evaluation of associations between risk factors and health outcomes in population s of patients ( 1 ) . The results of such studies improve our underst and ing of the clinical course of a disease and assist clinicians in making informed decisions about how best to manage patients . Prognostic research also informs the design of intervention studies by helping define subgroups of patients who may benefit from a new treatment and by providing necessary information about the natural history of a disorder ( 2 ) . There has recently been a rapid increase in the use of systematic review methods to synthesize the evidence on research questions related to prognosis . It is essential that investigators conducting systematic review s thoroughly appraise the method ologic quality of included studies to be confident that a study 's design , conduct , analysis , and interpretation have adequately reduced the opportunity for bias ( 3 , 4 ) . Caution is warranted , however , because inclusion of method ologically weak studies can threaten the internal validity of a systematic review ( 4 ) . This follows abundant empirical evidence that inadequate attention to biases can cause invalid results and inferences ( 5 - 9 ) . However , there is limited consensus on how to appraise the quality of prognosis studies ( 1 ) . A useful framework to assess bias in such studies follows the basic principles of epidemiologic research ( 10 , 11 ) . We focus on 6 areas of potential bias : study participation , study attrition , prognostic factor measurement , confounding measurement and account , outcome measurement , and analysis . The main objectives of our review of review s are to describe methods used to assess the quality of prognosis studies and to describe how well current practice s assess potential biases . Our secondary objective is to develop recommendations to guide future quality appraisal , both within single studies of prognostic factors and within systematic review s of the evidence . We hope this work facilitates future discussion and research on biases in prognosis studies and systematic review s. Methods Literature Search and Study Selection We identified systematic review s of prognosis studies by search ing MEDLINE ( 1966 to October 2005 ) using the search strategy recommended by McKibbon and colleagues ( 12 ) . This strategy combines broad search terms for systematic review s ( systematic review .mp ; meta- analysis .mp ) and a sensitive search strategy for prognosis studies ( cohort , incidence , mortality , follow-up studies , prognos * , predict * , or course ) . We also search ed the reference lists of included review s and method ologic papers to identify other relevant publications . We restricted our search to English- language publications . One review er conducted the search and selected the studies . Systematic review s , defined as review s of published studies with a comprehensive search and systematic selection , were included if they assessed the method ologic quality of the included studies by using 1 or more explicit criteria . We excluded studies if they were meta-analyses of independent patient data only , if their primary goal was to investigate the effectiveness of an intervention or specific diagnostic or screening tests , or if they included studies that were not done on humans . Data Extraction and Synthesis Individual items included in the quality assessment of the systematic review s were recorded as they were reported in the publication ( that is , the information that would be available to readers and future review ers ) . We review ed journal Web sites and contacted the authors of the systematic review s for additional information when authors made such an offer in their original papers . When review s assessed different study design s by using different sets of quality items , we extracted only those items used to assess cohort studies . We constructed a comprehensive list of distinct items that the review s used to assess the quality of their included studies . The full text of each review was screened . All items used by the review authors to assess the quality of studies were extracted into a computerized spreadsheet by 1 review er . Two experienced review ers , a clinical epidemiologist and an epidemiologist , independently synthesized the quality items extracted from the prognosis review s to determine how well the systematic review s assessed potential biases . We did this in 3 steps : 1 ) identified distinct concepts or domains addressed by the quality items ; 2 ) grouped each extracted quality item into the appropriate domain or domains ; and 3 ) identified the domains necessary to assess potential biases in prognosis studies . We then used this information to assess how well the review s ' quality assessment included items from the domains necessary to assess potential biases . After completing each of the first 3 steps , the review ers met to attempt to reach a consensus . The consensus process involved each review er presenting his or her observations and results , followed by discussion and debate . A third review er was available in cases of persistent disagreement or uncertainty . In the first step , all domains addressed by the quality items were identified . The first review er iteratively and progressively defined the domains as items were extracted from the included review s. The second review er defined domains from a r and om list of all extracted quality items . Limited guidance was provided to the review ers so that their assessment s and definitions of domains would be independent . The review ers agreed on a final set of domains that adequately and completely defined all of the extracted items . In the second step , review ers independently grouped each extracted item into the appropriate domains . Review ers considered each extracted item by asking , What is each particular quality item addressing ? or What are the review 's authors getting at with the particular quality assessment item ? . Items were grouped into the domain or domains that best represented the concepts being addressed . For example , the extracted items at least 80 % of the group originally identified was located for follow-up and follow-up was sufficiently complete or does n't jeopardize validity were each independently classified by both review ers as assessing the domain completeness of follow-up adequate , whereas the extracted item quantification and description of all subjects lost to follow-up was classified as assessing the domain completeness of follow-up described . In the third step , we identified the domains necessary to assess potential biases . Each review er considered the ability of the identified domains to adequately address , at least in part , 1 of the following 6 potential biases : 1 ) study participation , 2 ) study attrition , 3 ) prognostic factor measurement , 4 ) confounding measurement and account , 5 ) outcome measurement , and 6 ) analysis . Domains were considered to adequately address part of the framework if information garnered from that domain would inform the assessment of potential bias . For example , both review ers judged that the identified domain study population represents source population or population of interest assessed potential bias in a prognosis study , whereas the domain research question definition did not , although the latter is an important consideration in assessing the inclusion of studies in a systematic review . Finally , on the basis of our previous ratings , we looked at whether each review included items from the domains necessary to assess the 6 potential biases . We calculated the frequency of systematic review s by assessing each potential bias and the number of review s that adequately assessed bias overall . From this systematic synthesis , we developed recommendations for improving quality appraisal in future systematic review s of prognosis studies . We used Microsoft Access and Excel 2002 ( Microsoft Corp. , Redmond , Washington ) for data management and SAS for Windows , version 9.1 ( SAS Institute , Inc. , Cary , North Carolina ) for descriptive statistics . Role of the Funding Sources The funding sources , the Canadian Institutes of Health Research , the Canadian Chiropractic Research Foundation , the Ontario Chiropractic Association , and the Ontario Ministry of Health and Long Term Care , did not have a role in the collection , analysis , or interpretation of the data or in the decision to su bmi t the manuscript for publication . Results We identified 1384 potentially relevant articles . Figure 1 shows a flow chart of studies that were included and excluded . Figure 2 shows the number of review s identified by year of publication . We excluded 131 systematic review s of prognosis studies that did not seem to include any quality assessment of the included studies ; this represented 44 % of prognosis review s. We included 163 review s of prognosis studies in our analysis ( 13 - 175 ) . The most common topics were cancer ( 15 % ) , musculoskeletal disorders and rheumatology ( 13 % ) , cardiovascular ( 10 % ) , neurology ( 10 % ) , and obstetrics ( 10 % ) . Other review s included a wide range of health and health care topics . Sixty-three percent of the review s investigated the association between a specific prognostic factor and a particular outcome ; the remainder investigated multiple prognostic factors or models . The number of primary studies included in each systematic review ranged from 3 to 167 ( median , 18 [ interquartile range , 12 to 31 ] ) . A complete description of the included review s is available from the authors on request . Figure 1 . Flow diagram of inclusion and exclusion criteria of systematic review s. Figure 2 . Number of systematic review s of prognosis studies identified over time . Quality Items One hundred fifty-three review s provided adequate detail to allow extraction of quality items . Eight hundred eighty-two distinct quality items were extracted from the review s. Most review s developed their own set of quality items , with only a few applying criteria from previous review s. Most quality items We performed a Monte Carlo study to evaluate the effect of the number of events per variable ( EPV ) analyzed in logistic regression analysis . The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality ; the number of events per predictive variable was ( 252/7 =) 36 for the full sample . For the simulations , at values of EPV = 2 , 5 , 10 , 15 , 20 , and 25 , we r and omly generated 500 sample s of the 673 patients , chosen with replacement , according to a logistic model derived from the full sample . Simulation results for the regression coefficients for each variable in each group of 500 sample s were compared for bias , precision , and significance testing against the results of the model fitted to the original sample . For EPV values of 10 or greater , no major problems occurred . For EPV values less than 10 , however , the regression coefficients were biased in both positive and negative directions ; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coefficients ; the 90 % confidence limits about the estimated values did not have proper coverage ; the Wald statistic was conservative under the null hypothesis ; and paradoxical associations ( significance in the wrong direction ) were increased . Although other factors ( such as the total number of events , or sample size ) may influence the validity of the logistic model , our findings indicate that low EPV can lead to major problems Few studies have evaluated the factors influencing or predicting long-term survival after stroke in an unselected series of patients in whom the underlying cerebrovascular pathology is clearly defined . Moreover , the relative importance of risk factors for stroke , including sociodemographic and premorbid variables , has not been described in detail . Methods The study cohort consisted of 492 patients with stroke who were registered with a population -based study of acute cerebrovascular disease undertaken in Perth , Western Australia , during an 18-month period in 1989 and 1990 . Objective evidence of the pathological basis of the stroke was obtained in 86 % of cases , and all deaths among patients during a follow-up of 1 year were review ed . Results One hundred twenty patients ( 24 % ) died within 28 days of the onset of stroke . Among the different subtypes of stroke , the 1-year case fatality ( mean , 38 % ) varied from 6 % and 16 % for boundary zone infa rct ion and lacunar infa rct ion , respectively , to 42 % and 46 % for subarachnoid hemorrhage and primary intracerebral hemorrhage , respectively . Using Cox proportional-hazards analysis , a predictive model was developed on 321 patients with acute stroke ( test sample ) . The best model contained five baseline variables that were independent predictors of death within 1 year : coma ( relative risk [ RR ] , 3.0 ; 95 % confidence interval [ CI ] , 1.1 to 8.4 ) , urinary incontinence ( RR , 3.9 ; 95 % CI , 1.4 to 10.6 ) , cardiac failure ( RR , 6.5 ; 95 % CI , 2.8 to 15.1 ) , severe paresis ( RR , 4.9 ; 95 % CI , 1.6 to 15.5 ) , and atrial fibrillation ( RR , 2.0 ; 95 % CI , 1.1 to 3.5 ) . The sensitivity , specificity , and negative predictive value of this model for predicting death were 90 % , 83 % , and 95 % , respectively . When applied to a second r and omly selected validation sample of 171 events , sensitivity was 94 % , specificity 62 % , and negative predictive value 92 % , indicating stability of the model . Conclusions Although the case fatality , timing , and cause of death vary considerably among 
    Output:
    
Conclusion : The Six Simple Variables model performed well in six external validation studies , although prediction of outcome in patients with milder strokes was less reliable .