human
***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: 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 Objective : To describe associations between unintended weight loss ( UWL ) and characteristics of nutritional status . Design : A comparative cross-sectional assessment study at 11 sites in Europe . The target population was a stratified r and om sample of 4,455 recipients of home care ( 405 in each r and om sample from 11 urban areas ) aged 65 years and older . Measurements : the  Resident Assessment Instrument for Home Care , version 2.0 . Epidemiological and medical characteristics of clients and service utilisation were recorded in a st and ardized , comparative manner . UWL was defined as information of 5 % or more weight loss in the last 30 days ( or 10 % or more in the last 180 days ) . Results : The final sample consisted of 4,010 persons ; 74 % were female . The mean ages were 80.9 ± 7.5 years ( males ) and 82.8 ± 7.3 years ( females ) . No associations were found between single diagnoses and UWL , except for cancer . Cancer patients were excluded from further analyses . Persons with a Cognitive Performance Scale value ( CPS ) > 3 ( impaired ) had increased risk of UWL ( OR = 2.0 ) compared with those scoring ≤ 3 ( less impaired ) . Only in the oldest group did we find a significant association between UWL and reduction in ADL and IADL functions , comparing those who scored 3 or less with those who scored more than 3 ( disabled ) . A binary logistic regression model explained 26 % of UWL : less than one meal/day , reduced appetite , malnutrition , reduced social activity , experiencing a flare-up of a recurrent or chronic problem , and hospitalisation were important indicators . Conclusion : We recommend a regular comprehensive assessment in home care to identify clients with potential risk factors for weight loss and malnutrition , in particular those discharged from hospital , and those with physical dependency or cognitive problems . This study may provide incentives to create tailored preventive strategies Purpose Undernutrition is a prevalent problem in older , community-dwelling individuals . Aim of this study was to determine the effects of a dietetic treatment in older , undernourished , community-dwelling individuals . Methods A parallel r and omized controlled trial was performed in 146 non-institutionalized , undernourished individuals aged ≥65 years in primary care . Participants were r and omly assigned to the intervention ( referral to and treatment by a trained dietitian ) or control group ( no referral )  . Body weight , physical performance , h and grip strength , energy intake , protein intake and fat-free mass were assessed at baseline , after 3 months and after 6 months . Results All r and omized participants ( n = 146 ) were included in the intention-to-treat generalized estimating equations analysis ( 72 in intervention and 74 in control group ) . No treatment effect was found on the primary outcomes body weight ( β = 0.49 kg , 95 % CI : −0.15–1.12 ) , physical performance ( β = 0.15 points , 95 % CI : −0.33–0.64 ) and h and grip strength ( β = 0.49 kg , 95 % CI : −0.62–1.60 ) . Furthermore , no treatment effect was found for the secondary outcomes . Predefined subgroup analyses showed a treatment effect on body weight in physically active participants ( β = 1.25 kg , 95 % CI : 0.70–2.11 ) and not in inactive participants ( β = −0.20 kg , 95 % CI : −1.16–0.75 ) . Conclusions After 6 months , a dietetic treatment by trained dietitians does not lead to increases in body weight and physical functioning in older , undernourished , community-dwelling individuals Objectives To assess the association between Body Mass Index ( BMI ) and cause-specific mortality  in older adults and to assess which BMI was associated with lowest mortality . Design Prospect i ve study . Setting European towns . Participants 1,980 older adults , aged 70–75 years from the SENECA ( Survey in Europe on Nutrition and the Elderly : a concerted action ) study . Measurements  BMI  , examined in 1988/1989 , and mortality rates and causes of death during 10 years of follow-up . Results Cox proportional hazards model including both BMI and BMI 2 , accounting for sex , smoking status , educational level and age at baseline showed that BMI was associated with all-cause mortality ( p<0.01 ) , cardiovascular mortality ( p<0.01 ) and mortality from other causes ( p<0.01 ) , but not with cancer or respiratory mortality ( p>0.3 ) . The lowest all-cause mortality risk was found at 27.1 ( 95%CI 24.1 , 29.3 ) kg/m2 , and this risk was increased with statistical significance when higher than 31.4 kg/m2 and lower than 21.1 kg/m2 . The lowest cardiovascular mortality risk was found at 25.6 ( 95%CI 17.1 , 28.4 ) kg/m2 , and was increased with statistical significance when higher than 30.9 kg/m2 . Conclusion In this study , BMI was associated with all-cause mort 
    Output:
    
agent
***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: 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 Objective : To describe associations between unintended weight loss ( UWL ) and characteristics of nutritional status . Design : A comparative cross-sectional assessment study at 11 sites in Europe . The target population was a stratified r and om sample of 4,455 recipients of home care ( 405 in each r and om sample from 11 urban areas ) aged 65 years and older . Measurements : the  Resident Assessment Instrument for Home Care , version 2.0 . Epidemiological and medical characteristics of clients and service utilisation were recorded in a st and ardized , comparative manner . UWL was defined as information of 5 % or more weight loss in the last 30 days ( or 10 % or more in the last 180 days ) . Results : The final sample consisted of 4,010 persons ; 74 % were female . The mean ages were 80.9 ± 7.5 years ( males ) and 82.8 ± 7.3 years ( females ) . No associations were found between single diagnoses and UWL , except for cancer . Cancer patients were excluded from further analyses . Persons with a Cognitive Performance Scale value ( CPS ) > 3 ( impaired ) had increased risk of UWL ( OR = 2.0 ) compared with those scoring ≤ 3 ( less impaired ) . Only in the oldest group did we find a significant association between UWL and reduction in ADL and IADL functions , comparing those who scored 3 or less with those who scored more than 3 ( disabled ) . A binary logistic regression model explained 26 % of UWL : less than one meal/day , reduced appetite , malnutrition , reduced social activity , experiencing a flare-up of a recurrent or chronic problem , and hospitalisation were important indicators . Conclusion : We recommend a regular comprehensive assessment in home care to identify clients with potential risk factors for weight loss and malnutrition , in particular those discharged from hospital , and those with physical dependency or cognitive problems . This study may provide incentives to create tailored preventive strategies Purpose Undernutrition is a prevalent problem in older , community-dwelling individuals . Aim of this study was to determine the effects of a dietetic treatment in older , undernourished , community-dwelling individuals . Methods A parallel r and omized controlled trial was performed in 146 non-institutionalized , undernourished individuals aged ≥65 years in primary care . Participants were r and omly assigned to the intervention ( referral to and treatment by a trained dietitian ) or control group ( no referral )  . Body weight , physical performance , h and grip strength , energy intake , protein intake and fat-free mass were assessed at baseline , after 3 months and after 6 months . Results All r and omized participants ( n = 146 ) were included in the intention-to-treat generalized estimating equations analysis ( 72 in intervention and 74 in control group ) . No treatment effect was found on the primary outcomes body weight ( β = 0.49 kg , 95 % CI : −0.15–1.12 ) , physical performance ( β = 0.15 points , 95 % CI : −0.33–0.64 ) and h and grip strength ( β = 0.49 kg , 95 % CI : −0.62–1.60 ) . Furthermore , no treatment effect was found for the secondary outcomes . Predefined subgroup analyses showed a treatment effect on body weight in physically active participants ( β = 1.25 kg , 95 % CI : 0.70–2.11 ) and not in inactive participants ( β = −0.20 kg , 95 % CI : −1.16–0.75 ) . Conclusions After 6 months , a dietetic treatment by trained dietitians does not lead to increases in body weight and physical functioning in older , undernourished , community-dwelling individuals Objectives To assess the association between Body Mass Index ( BMI ) and cause-specific mortality  in older adults and to assess which BMI was associated with lowest mortality . Design Prospect i ve study . Setting European towns . Participants 1,980 older adults , aged 70–75 years from the SENECA ( Survey in Europe on Nutrition and the Elderly : a concerted action ) study . Measurements  BMI  , examined in 1988/1989 , and mortality rates and causes of death during 10 years of follow-up . Results Cox proportional hazards model including both BMI and BMI 2 , accounting for sex , smoking status , educational level and age at baseline showed that BMI was associated with all-cause mortality ( p<0.01 ) , cardiovascular mortality ( p<0.01 ) and mortality from other causes ( p<0.01 ) , but not with cancer or respiratory mortality ( p>0.3 ) . The lowest all-cause mortality risk was found at 27.1 ( 95%CI 24.1 , 29.3 ) kg/m2 , and this risk was increased with statistical significance when higher than 31.4 kg/m2 and lower than 21.1 kg/m2 . The lowest cardiovascular mortality risk was found at 25.6 ( 95%CI 17.1 , 28.4 ) kg/m2 , and was increased with statistical significance when higher than 30.9 kg/m2 . Conclusion In this study , BMI was associated with all-cause mort 
    Output:
    