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 Objectives To establish a protocol for the treatment of  fragility fractures in the hospital setting based on treatment of osteoporosis . Material s and methods An intervention protocol was implemented in patients with fragility fractures based on ( 1 ) indicating the diagnosis of osteoporotic fracture in the summary of discharge ; ( 2 ) “ lifestyle recommendations ” ; and ( 3 ) therapy for osteoporosis .  Thirty-one hospitals were involved and they were informed of the importance of protocol compliance . In the first phase , a retrospective study was conducted to establish the number of low-energy fractures treated and the percentage of them that had complied with the protocol ( n = 887 ) . Then , prospect ively , the same data were collected for the patients managed for 1 year ( n = 6,826 ) in three sections of 4-month intervals . Results The percentage of compliance increased from 8.2 to 57.2 % in the first point , from 12.6 to 42.4 % in the second , and from 10.3 to 43.2 % in the third . Conclusion The implementation of programs to improve osteoporosis treatment is very useful for ensuring adherence in the management of osteoporosis following admission due to fragility fracture BACKGROUND Although osteoporosis is strongly associated with hip fractures , the initiation of osteoporosis treatment following hip fractures occurs at surprisingly low rates of between 5 % and 30 % . Currently , most patients receiving treatment have been referred back to their primary care physician for osteoporosis management . The purpose of this study was to compare the effect of osteoporosis management initiated by the orthopaedic team and osteoporosis management initiated by the primary care physician on the rates of treatment at six months . METHODS A prospect i ve r and omized trial was conducted to assess the difference in the rate of osteoporosis treatment  when an in-house assessment of osteoporosis was initiated by the orthopaedic surgeon and follow-up was conducted in a specialized orthopaedic osteoporosis clinic compared with osteoporosis education and " usual " care . RESULTS Sixty-two patients were enrolled in the study . Thirty-one patients each were in the control and intervention groups . The percentage of patients who were on pharmacologic treatment for osteoporosis  at six months after the fracture was significantly greater when the evaluation was initiated by the orthopaedic surgeon and was managed in a specialized orthopaedic osteoporosis clinic ( 58 % ) than when treatment was managed by a primary care physician ( 29 % ) ( p = 0.04 ) . CONCLUSIONS An active role by orthopaedic surgeons in the management of osteoporosis improves the rate of treatment at six months following a hip fracture BACKGROUND In a r and omized trial of patients with hip fractures , we previously demonstrated that a hospital-based case manager could increase rates of appropriate osteoporosis treatment to 51 % compared with 22 % for usual care ( P < .001 ) . Alongside that trial , we conducted an economic analysis . METHODS Patients with hip fractures were r and omized to usual care ( n = 110 ) or a case manager ( n = 110 ) and followed up for 1 year . Time-motion studies were used to determine intervention costs . From a third-party health care payer perspective and over the patient 's remaining lifetime , a Markov decision-analytic model was constructed to determine  cost-effectiveness  of the intervention compared with usual care .  Costs and benefits were discounted at 3 % and expressed in 2006 Canadian dollars . RESULTS The intervention cost CaD $ 56 per patient . Compared with usual care , the intervention strategy was dominant : for every 100 patients case managed , 6 fractures ( 4 hip fractures ) were prevented , 4 quality -adjusted life-years were gained , and CaD $ 260 000 was saved by the health care system . Irrespective of the number of patients case managed , the intervention reached a break-even threshold within 2 years . The intervention dominated usual care over the entire spectrum of 1-way sensitivity analyses and was cost-saving in 82 % of probabilistic model simulations . CONCLUSIONS Compared with usual care , we found that using a case manager for patients with hip fractures increased rates of appropriate osteoporosis treatment . The intervention dominated usual care , and the analysis suggests that systems implementing an intervention similar to ours should expect to see a reduction in fractures , gains in life expectancy , and substantial cost savings . TRIAL REGISTRATION clinical trials.gov Identifier : NCT00175175 Background : Osteoporosis is a common disease characterized by decreased bone mass and increased fracture risk in postmenopausal women and the elderly . Hip fractures are among the most common consequences of osteoporosis and unfortunately usually occur late in the course of the disease . When a patient is admitted to the hospital with a fragility hip fracture , a unique opportunity for diagnosis and treatment presents itself . Fortunately , several medications have proven to be effective in lowering the risk of future fractures . The purpose s of the present study were to test the hypothesis that most fragility hip fractures go untreated and to determine whether educational efforts to raise physician awareness have led to an improvement in osteoporosis treatment rates . Methods : A retrospective cohort study was performed with use of the patient data bases at two university medical centers 
    Output:
    
agent
Conclusion Most interventions did not report key outcomes .
***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 Objectives To establish a protocol for the treatment of  fragility fractures in the hospital setting based on treatment of osteoporosis . Material s and methods An intervention protocol was implemented in patients with fragility fractures based on ( 1 ) indicating the diagnosis of osteoporotic fracture in the summary of discharge ; ( 2 ) “ lifestyle recommendations ” ; and ( 3 ) therapy for osteoporosis .  Thirty-one hospitals were involved and they were informed of the importance of protocol compliance . In the first phase , a retrospective study was conducted to establish the number of low-energy fractures treated and the percentage of them that had complied with the protocol ( n = 887 ) . Then , prospect ively , the same data were collected for the patients managed for 1 year ( n = 6,826 ) in three sections of 4-month intervals . Results The percentage of compliance increased from 8.2 to 57.2 % in the first point , from 12.6 to 42.4 % in the second , and from 10.3 to 43.2 % in the third . Conclusion The implementation of programs to improve osteoporosis treatment is very useful for ensuring adherence in the management of osteoporosis following admission due to fragility fracture BACKGROUND Although osteoporosis is strongly associated with hip fractures , the initiation of osteoporosis treatment following hip fractures occurs at surprisingly low rates of between 5 % and 30 % . Currently , most patients receiving treatment have been referred back to their primary care physician for osteoporosis management . The purpose of this study was to compare the effect of osteoporosis management initiated by the orthopaedic team and osteoporosis management initiated by the primary care physician on the rates of treatment at six months . METHODS A prospect i ve r and omized trial was conducted to assess the difference in the rate of osteoporosis treatment  when an in-house assessment of osteoporosis was initiated by the orthopaedic surgeon and follow-up was conducted in a specialized orthopaedic osteoporosis clinic compared with osteoporosis education and " usual " care . RESULTS Sixty-two patients were enrolled in the study . Thirty-one patients each were in the control and intervention groups . The percentage of patients who were on pharmacologic treatment for osteoporosis  at six months after the fracture was significantly greater when the evaluation was initiated by the orthopaedic surgeon and was managed in a specialized orthopaedic osteoporosis clinic ( 58 % ) than when treatment was managed by a primary care physician ( 29 % ) ( p = 0.04 ) . CONCLUSIONS An active role by orthopaedic surgeons in the management of osteoporosis improves the rate of treatment at six months following a hip fracture BACKGROUND In a r and omized trial of patients with hip fractures , we previously demonstrated that a hospital-based case manager could increase rates of appropriate osteoporosis treatment to 51 % compared with 22 % for usual care ( P < .001 ) . Alongside that trial , we conducted an economic analysis . METHODS Patients with hip fractures were r and omized to usual care ( n = 110 ) or a case manager ( n = 110 ) and followed up for 1 year . Time-motion studies were used to determine intervention costs . From a third-party health care payer perspective and over the patient 's remaining lifetime , a Markov decision-analytic model was constructed to determine  cost-effectiveness  of the intervention compared with usual care .  Costs and benefits were discounted at 3 % and expressed in 2006 Canadian dollars . RESULTS The intervention cost CaD $ 56 per patient . Compared with usual care , the intervention strategy was dominant : for every 100 patients case managed , 6 fractures ( 4 hip fractures ) were prevented , 4 quality -adjusted life-years were gained , and CaD $ 260 000 was saved by the health care system . Irrespective of the number of patients case managed , the intervention reached a break-even threshold within 2 years . The intervention dominated usual care over the entire spectrum of 1-way sensitivity analyses and was cost-saving in 82 % of probabilistic model simulations . CONCLUSIONS Compared with usual care , we found that using a case manager for patients with hip fractures increased rates of appropriate osteoporosis treatment . The intervention dominated usual care , and the analysis suggests that systems implementing an intervention similar to ours should expect to see a reduction in fractures , gains in life expectancy , and substantial cost savings . TRIAL REGISTRATION clinical trials.gov Identifier : NCT00175175 Background : Osteoporosis is a common disease characterized by decreased bone mass and increased fracture risk in postmenopausal women and the elderly . Hip fractures are among the most common consequences of osteoporosis and unfortunately usually occur late in the course of the disease . When a patient is admitted to the hospital with a fragility hip fracture , a unique opportunity for diagnosis and treatment presents itself . Fortunately , several medications have proven to be effective in lowering the risk of future fractures . The purpose s of the present study were to test the hypothesis that most fragility hip fractures go untreated and to determine whether educational efforts to raise physician awareness have led to an improvement in osteoporosis treatment rates . Methods : A retrospective cohort study was performed with use of the patient data bases at two university medical centers 
    Output:
    