***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 Although visual neglect is a predictor of poor outcome after stroke , some patients regain independence , whilst others take up considerable rehabilitation re sources . Intensive treatment of visual neglect is available and a knowledge of the predictive features in the recovery of these patients would be helpful in the early selection of patients for treatment . A study was therefore carried out to determine the prognosis of patients presenting with visual neglect at two to three days after stroke . Linear logistic regression showed that the initial degree of paralysis ( measured by the Motricity Index ) , the severity of neglect ( measured by the Visual Neglect Recovery Index ) and the patient 's age were the significant predictors of independence ( Barthel score 20 ) , mild dependence ( Barthel 15 - 19 ) , and moderate/severe dependence ( Barthel 0 - 14 ) in surviving patients at three months and at six months . Regression equations correctly predicted 78 % of outcomes , and had a sensitivity and specificity for " independence " of 84 % and 90 % respectively , and a sensitivity and specificity for " moderate/severe dependence " of 89 % and 80 % . It is suggested that these equations may be useful in selecting comparable groups of patients for r and omised controlled trials of treatment of visual neglect A cohort study tracks two or more groups forward from exposure to outcome . This type of study can be done by going ahead in time from the present ( prospect i ve cohort study ) or , alternatively , by going back in time to comprise the cohorts and following them up to the present ( retrospective cohort study ) . A cohort study is the best way to identify incidence and natural history of a disease , and can be used to examine multiple outcomes after a single exposure . However , this type of study is less useful for examination of rare events or those that take a long time to develop . A cohort study should provide specific definitions of exposures and outcomes : determination of both should be as objective as possible . The control group ( unexposed ) should be similar in all important respects to the exposed , with the exception of not having the exposure . Observational studies , however , rarely achieve such a degree of similarity , so investigators need to measure and control for confounding factors . Reduction of loss to follow-up over time is a challenge , since differential losses to follow-up introduce bias . Variations on the cohort theme include the before-after study and nested case-control study ( within a cohort study ) . Strengths of a cohort study include the ability to calculate incidence rates , relative risks , and 95 % CIs . This format is the preferred way of presenting study results , rather that with p values Background and Purpose — Prediction models for ischemic stroke outcome have the potential to contribute prognostic information in the clinical and /or research setting . The importance of diffusion-weighted magnetic resonance imaging ( DWI ) in the prediction of clinical outcome , however , is unclear . The purpose of this study was to combine acute clinical data and DWI lesion volume for ischemic stroke patients to determine whether DWI improves the prediction of clinical outcome . Methods — Patients ( N=382 ) with baseline DWI data from the Glycine Antagonist In Neuroprotection and citicoline ( 010 and 018 ) trials were used to develop the prediction models by multivariable logistic regression .  Data from prospect ively collected patients ( N=266 ) from the Acute Stroke Accurate Prediction Study were used to externally vali date the model equations . The models predicted either full recovery or nursing home – level disability/death , as defined by the National Institutes of Health Stroke Scale , Barthel Index , or modified Rankin Scale . Results — The full-recovery models with DWI lesion volume had areas under the receiver operating characteristic curves ( AUCs ) of 0.799 to 0.821 , and those without DWI lesion volume had AUCs of 0.758 to 0.798 . The nursing home – level disability/death models with DWI had AUCs of 0.832 to 0.882 , and those without DWI 
    Output:
    
Because of insufficient method ological quality of most prognostic studies , the predictive value of many clinical determinants for outcome of ADL remains unclear .