***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 — The benefits of medication adherence to control cardiovascular disease ( CVD ) are well defined , yet multiple studies have identified poor adherence . The influence of life chaos on medication adherence is unknown . Because this is a novel application of an instrument , our preliminary objective was to underst and patient factors associated with chaos . The main objective was to evaluate the extent to which an instrument design ed to measure life chaos is associated with CVD-medication nonadherence . Methods and Results — Using baseline data from an ongoing r and omized trial to improve postmyocardial infa rct ion ( MI ) management , multivariable logistic regression identified the association between life chaos and CVD-medication nonadherence . Patients had hypertension and a myocardial infa rct ion in the past 3 years ( n=406 ) . Nearly 43 % reported CVD-medication nonadherence in the past month . In simple linear regression , the following were associated with higher life chaos : medication nonadherence ( & bgr;=1.86 ; 95 % confidence interval [ CI ] , 0.96–2.76 ) , female sex ( & bgr;=1.22 ; 95 % CI [ 0.22–2.24 ] ) , minority race ( & bgr;=1.72 ; 95 % CI [ 0.78–2.66 ] ) , having less than high school education ( & bgr;=2.05 ; 95 % CI [ 0.71–3.39 ] ) , low health literacy ( & bgr;=2.06 ; 95 % CI [ 0.86–3.26 ] ) , and inadequate financial status ( & bgr;=1.93 ; 95 % CI [ 0.87–3.00 ] ) . Being married ( & bgr;=−2.09 , 95 % CI [ −3.03 to −1.15 ] ) was associated with lower life chaos . As chaos quartile increased , patients exhibited more nonadherence . In logistic regression , adjusting for sex , race , marital status , employment , education , health literacy , and financial status , a 1-unit life chaos increase was associated with a 7 % increase ( odds ratio , 1.07 ; 95 % CI [ 1.02–1.12 ] ) in odds of reporting medication nonadherence . Conclusions — Our results suggest that life chaos may be an important determinant of medication adherence . Life chaos screenings could identify those at risk for nonadherence . Clinical Trial Registration — URL : http://www . clinical trials.gov . Unique identifier : Background Adherence to evidence based medicines in patients who have experienced a myocardial infa rct ion remains low . Individual ’s beliefs towards their medicines are a strong predictor of adherence and may influence other factors that impact on adherence . Objective To investigate if community pharmacists discussing patients ’ beliefs about their medicines improved medication adherence at 12 months post myocardial infa rct ion . Setting This study included 200 patients discharged from a public teaching hospital in Queensl and , Australia , following a myocardial infa rct ion . Patients were r and omised into intervention ( n = 100 ) and control groups ( n = 100 ) and followed for 12 months . Method All patients were interviewed between 5 to 6 weeks , at 6 and 12 months post discharge by the research er using the repertory grid technique . This technique was used to elicit the patient ’s individualised beliefs about their medicines for their myocardial infa rct ion . In the intervention group , patients ’ beliefs about their medicines were communicated by the research er to their community pharmacist . The pharmacist used this information to tailor their discussion with the patient about their medication beliefs at design ated time points ( 3 and 6 months post discharge ) . The control group was provided with usual care . Main outcome measure The difference in non-adherence measured using a medication possession ratio between the intervention and control groups at 12 months post myocardial infa rct ion . Results There were 137 patients remaining in the study ( intervention group n = 72 , control group n = 65 ) at 12 months . In the intervention group 29 % ( n = 20 ) of patients were non-adherent compared to 25 % ( n = 16 ) of patients in control group . Conclusion Discussing patients ’ beliefs about their medicines for their myocardial infa rct ion did not improve medication adherence . Further research on patients beliefs should focus on targeting non-adherent patients whose reasons for their non-adherence is driven by their medication beliefs 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 BACKGROUND Adherence to evidence -based cardiovascular ( CV ) medications after an acute myocardial infa rct ion ( MI ) is low after the first 6 months . The use of fixed-dose combinations ( FDC ) has been shown to improve treatment adherence and risk factor control . However , no previous r and omized trial has analyzed the impact of a polypill strategy on adherence in post-MI patients . OBJECTIVES The cross-sectional FOCUS ( Fixed-Dose Combination Drug for Secondary Cardiovascular Prevention ) study ( Phase 1 ) aim ed to eluci date factors that interfere with appropriate adherence to CV medications for secondary prevention after an acute MI . Additionally , 6 
    Output:
    
There was some evidence that psychosocial factors , particularly depression , were associated with medication adherence following ACS .