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: Risk prediction for electrocardiographic ( ECG ) left ventricular hypertrophy related criteria , used in clinical trials , and epidemiologic studies of clinical ly healthy people , has depended in the past on dichotomous classification of ECG LVH criteria . Recent analyses have shown that more sensitive methods of LVH ECG classification without loss of specificity are needed to improve on dichotomous classification . This was done by relating six year incident significant change in continuous score criteria of ECG LVH to the 16 year ( 10 year post trial ) coronary heart disease ( CHD ) and cardiovascular disease ( CVD ) mortality among 12,866 men , free of clinical disease , aged 35 to 57 years at baseline in the Multiple Risk Factor Intervention Trial . It was found that significant change in continuous ECG LVH criteria was a stronger independent predictor of future CHD and CVD mortality than was use of dichotomous classification of the same criteria . It was also demonstrated that increase in continuous ECG LVH indexes , below previous dichotomous thresholds independently ( of st and ard CVD risk factors , including increase in obesity-indicated by an increase in adult BMI ) predicted excess CHD and CVD mortality and that combinations of continuous indices increases the specificity and relative risk in clinical ly disease-free middle-aged men 1 . Frei B , Engl and L , Ames BN . Ascorbate is an outst and ing antioxidant in human blood plasma . Proc Natl Acad Sci U S A 1989;86:6377–6381 . 2 . Ting HH , Timimi FK , Boles KS , Creager SJ , Ganz P , Creager MA . Vitamin C improves endothelium-dependent vasodilation in patients with non-insulindependent diabetes mellitus . J Clin Invest 1996;97:22–28 . 3 . Heitzer T , Just H , Munzel T. Antioxidant vitamin C improves endothelial dysfunction in chronic smokers . Circulation 1996;94:6–9 . 4 . Slakey D , Roza A , Pieper G , Johnson C , Adams M. Delayed cardiac allograft rejection due to combined cyclosporine and antioxidant therapy . Transplantation 1993;56:1305–1309 . 5 . Fish RD , Nabel EG , Selwyn AP , Ludmer PL , Mudge GH , Kirshenbaum JM , Schoen FJ , Alex and er RW , Ganz P. Responses of coronary arteries of cardiac transplant patients to acetylcholine . J Clin Invest 1988;81:21–31 . 6 . Davis SF , Yeung AC , Meredith IT , Charbonneau F , Ganz P , Selwyn AP , And erson TJ . Early endothelial dysfunction predicts the development of transplant coronary artery disease at 1 year posttransplant . Circulation 1996;93:457– 462 . 7 . Morrow JD , Roberts LJ II . Mass spectrometric quantification of F2-isoprostanes in biological fluids and tissues as measure of oxidant stress . Methods Enzymol 1999;300:3–12 . 8 . Watanabe H , Kakihana M , Ohtsuka S , Sugishita Y. R and omized , double-blind , placebo-controlled study of ascorbate on the preventive effect of nitrate tolerance in patients with congestive heart failure . Circulation 1998;97:886–891 Key Summary Points Risk prediction models are statistical models used to predict the probability of an outcome on the basis of the values of 1 or more risk factors ( markers ) . The accuracy of the model 's predictions is typically summarized with statistics that describe the model 's discrimination and calibration . Risk stratification tables are a more informative way to assess and compare the models . The tables illustrate the distribution of predictions across risk categories . That illustration allows users to assess 3 key measures of the models ' value for guiding medical decisions : the models ' calibration , ability to stratify people into clinical ly relevant risk categories , and accuracy at classifying patients into higher- and lower-risk categories . This information is contained in the margins of the risk stratification table rather than in its cells . The tables should only be used to compare risk prediction models when one of the models contains all of the markers that are contained in the other ( nested models ) ; they should not be used to compare models with different sets of markers ( nonnested models ) . The table predictions require corrections when casecontrol data are used . The recent epidemiologic and clinical literature is filled with studies evaluating statistical models that predict risk for disease or some other adverse event ( 15 ) . Because risk prediction models are intended to help patients and clinicians make decisions , evaluation of these models requires methods that differ from those used to assess models describing disease etiology . This is because the characteristics of the models are less important than their value for guiding decisions . Cook and colleagues ( 1 , 6 ) recently proposed a new approach to evaluate risk prediction models : a risk stratification table . This methodology appropriately focuses on the key purpose of a risk prediction model , which is to classify individuals into clinical ly relevant risk categories , and it has therefore been widely adopted in the literature ( 24 ) . We examine the risk stratification approach in detail in this article , identifying the relevant information that can be abstract ed from a risk stratification table and caution ing against misuses of the method that frequently occur in practice . We use a recently published study of a breast cancer risk prediction model by Tice and colleagues ( 2 ) to illustrate the concepts . Background A risk prediction marker is any measure that is used to predict a person 's risk for an event . It may be a quantitative measure , such as high-density lipoprotein cholesterol level , or a qualitative measure , such as family history of disease . Risk predictors are also risk factors , in the sense that they will necessarily be strongly associated with the risk for disease . But a large , significant association does not assure that the marker has value in predicting risk for many people . A risk prediction model is a statistical model that combines information from several markers . Common types include logistic regression models , Cox proportional hazard models , and classification trees . Each type of model produces a predicted risk for each person by using information in the model . Consider , for example , a model predicting breast cancer risk that includes age as the only predictor . The result ing risk prediction for a woman of a given age is simply the proportion of women her age who develop breast cancer . The woman 's predicted risk will change if more information is included in the model . For instance , if family history information is added , her predicted risk will be the proportion of women her age and with her family history who develop breast cancer . The purpose of a risk prediction model is to accurately stratify individuals into clinical ly relevant risk categories . This risk information can be used to guide clinical or policy decisions , for example , about preventive interventions for persons or disease screening for sub population s identified as high risk , or to select persons for inclusion in clinical trials . The value of a risk prediction model for guiding these kinds of decisions can be judged by the extent to which the risk calculated from the model reflects the fraction of persons in the population with actual events ( its calibration ) ; the proportions in which the population is stratified into clinical ly relevant risk categories ( its stratification capacity ) ; and the extent to which participants with events are assigned to high-risk categories and those without events are assigned to low-risk categories ( its classification accuracy ) . Risk prediction models are commonly evaluated by using the receiver-operating characteristic ( ROC ) curve ( 4 , 7 ) , which is a st and ard tool for evaluating the discriminatory accuracy of diagnostic or screening markers . This curve shows the true-positive rate plotted against the false-positive rate for rules that classify persons by using risk thresholds that vary over allpossible values . Receiver-operating characteristic curves are generally not helpful for evaluating risk prediction models because they do not provide information about the actual risks that the models predict or about the proportion of participants who have high or low risk values . Moreover , when comparing ROC curves for 2 risk prediction models , the models are aligned according to their false-positive rates ( that is , different risk thresholds are applied to the 2 models to achieve the same false-positive rate ) . This is clearly inappropriate . In addition , the area under the ROC curve or c-statistic , a commonly reported summary measure that can be interpreted as the probability that the predicted risk for a participant with an event is higher than that for a participant without an event , has little direct clinical relevance . Clinicians are never asked to compare risks for a pair of patients one who will eventually have the event and one who will not . Neither the ROC curve nor the c-statistic relates to the practical task of predicting risks for clinical decision making . Cook and colleagues ( 1 , 6 ) propose using risk stratification tables to evaluate the incremental value of a new marker , or the benefit of adding a new marker ( for example , C-reactive protein ) , to an established set of risk predictors ( for example , Framingham risk predictors , such as age , diabetes , cholesterol level , smoking , and low-density lipoprotein cholesterol levels ) . In these stratification tables , risks calculated from models with and without the new marker are cross-tabulated . This approach represents a substantial improvement over the use of ROC methodology because it displays the risks calculated by use of the model and the proportions of individuals in the population who are stratified into the risk groups . We will provide an example of this approach and show how information about model calibration , stratification capacity , and classification accuracy can be derived from a risk stratification table and used to assess the added value of a marker for clinical and health care policy decisions . Example Tice and colleagues ( 2 ) published a study that builds and evaluates a model for predicting breast cancer risk by using data from 1095484 women in a prospect i ve cohort and incidence data from the Surveillance , Epidemiology , and End Results data base . Age , race or ethnicity , family history , and history of breast biopsy were used to model risk with a Cox proportional hazard model . The study focused on the benefit of adding breast density information to the model . The hazard ratio for breast density in the multivariate model ( extremely dense vs. almost entirely fat ) was estimated as 4.2 for women younger than age 65 years and 2.2 for women age 65 years or older . This suggests that breast density is strongly associated with disease riskthat is , that breast cancer rates are higher among women with higher breast density . However , it does not describe the value of breast density for helping women make informed clinical decisions , which requires knowledge of the frequency distribution of breast density in the population . To evaluate the added value of breast density , Tice and colleagues defined 5-year breast cancer risk categories as low ( # # lt##1 % ) , low to intermediate ( 1 % to 1.66 % ) , intermediate to high ( 1.67 % to 2.5 % ) , and high ( # # gt##2.5 % ) . The 1.67 % cutoff for intermediate risk was presumably chosen on the basis of recommendations by the American Society of Clinical Oncology ( 8) and the Canadian Task Force on Preventive Health Care ( 9 ) to counsel women with 5-year risks greater than this threshold about considering tamoxifen for breast cancer prevention . Tice and colleagues used a risk stratification table ( Table 1 ) to compare risk prediction models with and without breast density . Table 1 . Five-Year Risks for Breast Cancer as Predicted by Models That Do and Do Not Include Breast Density Calibration Assessing model calibration is an important first step in evaluating any risk prediction model . Good calibration is essential ; it means that the model-predicted probability of an event for a person with specified predictor values is the same as or very close to the proportion of all persons in the population with those same predictor values who experience the event ( 10 ) . With many predictors , and especially with continuous predictors , we can not evaluate calibration at each possible predictor value because there are too few participants with exactly those values . Instead , the st and ard approach is to place persons within categories of predicted risk and to compare the category values with the observed event rates for participants in each category . The calibration of the risk prediction models for breast cancer can be assessed by comparing the proportions of events in the margins of Table 1 with the corresponding row and column labels . For the model without breast density , the proportions of observed events within each risk category are in the far-right Total column and they generally agree OBJECTIVE We investigated the prognostic significance of risk scores and exercise workload with respect to stroke . Background . There are no data on exercise workload combined with European Systematic Coronary Risk Evaluation ( SCORE ) in the prediction of stroke . METHODS  Exercise workload was measured by exercise test with an electrically braked cycle ergometer performed at baseline . The study is based on a r and om population -based sample of 1639 men ( 42 - 60 years ) without history of type 2 diabetes or atherosclerotic cardiovascular disease including coronary heart disease , stroke or claudication . RESULTS During an average follow-up of 16 years , a total of 97 strokes occurred , of which 71 were ischaemic strokes . Independent predictors for all strokes were European SCORE [ for 1 % increment , relative risk ( RR ) : 1.12 , 95 % CI : 1.02 to 1.22 , P=0.017 ) , maximal workload ( for 20 W increment , RR : 0.87 , 95 % CI : 0.80 to 0.95 , P=0.003 ) and body mass index ( for 5 kg m(-2 ) increment , RR : 1.08 , 95 % CI : 1.03 to 1.14 , P=0.004 ) , when adjusted for serum HDL , alcohol consumption , C-reactive protein , family history of coronary heart disease , exercise-induced ST changes and the use of medications for hypertension , dyslipidaemia or aspirin . The risk was 2.54-fold ( 95 % CI : 1.27 - 5.09 , P=0.008 ) for any strokes and 4.43-fold ( 95 % CI 1.69 - 11.78 , 
    Output:
    
agent
Evidence on harms was limited , but direct harms seemed minimal ( for resting ECG ) or small ( for exercise ECG ) .
***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: Risk prediction for electrocardiographic ( ECG ) left ventricular hypertrophy related criteria , used in clinical trials , and epidemiologic studies of clinical ly healthy people , has depended in the past on dichotomous classification of ECG LVH criteria . Recent analyses have shown that more sensitive methods of LVH ECG classification without loss of specificity are needed to improve on dichotomous classification . This was done by relating six year incident significant change in continuous score criteria of ECG LVH to the 16 year ( 10 year post trial ) coronary heart disease ( CHD ) and cardiovascular disease ( CVD ) mortality among 12,866 men , free of clinical disease , aged 35 to 57 years at baseline in the Multiple Risk Factor Intervention Trial . It was found that significant change in continuous ECG LVH criteria was a stronger independent predictor of future CHD and CVD mortality than was use of dichotomous classification of the same criteria . It was also demonstrated that increase in continuous ECG LVH indexes , below previous dichotomous thresholds independently ( of st and ard CVD risk factors , including increase in obesity-indicated by an increase in adult BMI ) predicted excess CHD and CVD mortality and that combinations of continuous indices increases the specificity and relative risk in clinical ly disease-free middle-aged men 1 . Frei B , Engl and L , Ames BN . Ascorbate is an outst and ing antioxidant in human blood plasma . Proc Natl Acad Sci U S A 1989;86:6377–6381 . 2 . Ting HH , Timimi FK , Boles KS , Creager SJ , Ganz P , Creager MA . Vitamin C improves endothelium-dependent vasodilation in patients with non-insulindependent diabetes mellitus . J Clin Invest 1996;97:22–28 . 3 . Heitzer T , Just H , Munzel T. Antioxidant vitamin C improves endothelial dysfunction in chronic smokers . Circulation 1996;94:6–9 . 4 . Slakey D , Roza A , Pieper G , Johnson C , Adams M. Delayed cardiac allograft rejection due to combined cyclosporine and antioxidant therapy . Transplantation 1993;56:1305–1309 . 5 . Fish RD , Nabel EG , Selwyn AP , Ludmer PL , Mudge GH , Kirshenbaum JM , Schoen FJ , Alex and er RW , Ganz P. Responses of coronary arteries of cardiac transplant patients to acetylcholine . J Clin Invest 1988;81:21–31 . 6 . Davis SF , Yeung AC , Meredith IT , Charbonneau F , Ganz P , Selwyn AP , And erson TJ . Early endothelial dysfunction predicts the development of transplant coronary artery disease at 1 year posttransplant . Circulation 1996;93:457– 462 . 7 . Morrow JD , Roberts LJ II . Mass spectrometric quantification of F2-isoprostanes in biological fluids and tissues as measure of oxidant stress . Methods Enzymol 1999;300:3–12 . 8 . Watanabe H , Kakihana M , Ohtsuka S , Sugishita Y. R and omized , double-blind , placebo-controlled study of ascorbate on the preventive effect of nitrate tolerance in patients with congestive heart failure . Circulation 1998;97:886–891 Key Summary Points Risk prediction models are statistical models used to predict the probability of an outcome on the basis of the values of 1 or more risk factors ( markers ) . The accuracy of the model 's predictions is typically summarized with statistics that describe the model 's discrimination and calibration . Risk stratification tables are a more informative way to assess and compare the models . The tables illustrate the distribution of predictions across risk categories . That illustration allows users to assess 3 key measures of the models ' value for guiding medical decisions : the models ' calibration , ability to stratify people into clinical ly relevant risk categories , and accuracy at classifying patients into higher- and lower-risk categories . This information is contained in the margins of the risk stratification table rather than in its cells . The tables should only be used to compare risk prediction models when one of the models contains all of the markers that are contained in the other ( nested models ) ; they should not be used to compare models with different sets of markers ( nonnested models ) . The table predictions require corrections when casecontrol data are used . The recent epidemiologic and clinical literature is filled with studies evaluating statistical models that predict risk for disease or some other adverse event ( 15 ) . Because risk prediction models are intended to help patients and clinicians make decisions , evaluation of these models requires methods that differ from those used to assess models describing disease etiology . This is because the characteristics of the models are less important than their value for guiding decisions . Cook and colleagues ( 1 , 6 ) recently proposed a new approach to evaluate risk prediction models : a risk stratification table . This methodology appropriately focuses on the key purpose of a risk prediction model , which is to classify individuals into clinical ly relevant risk categories , and it has therefore been widely adopted in the literature ( 24 ) . We examine the risk stratification approach in detail in this article , identifying the relevant information that can be abstract ed from a risk stratification table and caution ing against misuses of the method that frequently occur in practice . We use a recently published study of a breast cancer risk prediction model by Tice and colleagues ( 2 ) to illustrate the concepts . Background A risk prediction marker is any measure that is used to predict a person 's risk for an event . It may be a quantitative measure , such as high-density lipoprotein cholesterol level , or a qualitative measure , such as family history of disease . Risk predictors are also risk factors , in the sense that they will necessarily be strongly associated with the risk for disease . But a large , significant association does not assure that the marker has value in predicting risk for many people . A risk prediction model is a statistical model that combines information from several markers . Common types include logistic regression models , Cox proportional hazard models , and classification trees . Each type of model produces a predicted risk for each person by using information in the model . Consider , for example , a model predicting breast cancer risk that includes age as the only predictor . The result ing risk prediction for a woman of a given age is simply the proportion of women her age who develop breast cancer . The woman 's predicted risk will change if more information is included in the model . For instance , if family history information is added , her predicted risk will be the proportion of women her age and with her family history who develop breast cancer . The purpose of a risk prediction model is to accurately stratify individuals into clinical ly relevant risk categories . This risk information can be used to guide clinical or policy decisions , for example , about preventive interventions for persons or disease screening for sub population s identified as high risk , or to select persons for inclusion in clinical trials . The value of a risk prediction model for guiding these kinds of decisions can be judged by the extent to which the risk calculated from the model reflects the fraction of persons in the population with actual events ( its calibration ) ; the proportions in which the population is stratified into clinical ly relevant risk categories ( its stratification capacity ) ; and the extent to which participants with events are assigned to high-risk categories and those without events are assigned to low-risk categories ( its classification accuracy ) . Risk prediction models are commonly evaluated by using the receiver-operating characteristic ( ROC ) curve ( 4 , 7 ) , which is a st and ard tool for evaluating the discriminatory accuracy of diagnostic or screening markers . This curve shows the true-positive rate plotted against the false-positive rate for rules that classify persons by using risk thresholds that vary over allpossible values . Receiver-operating characteristic curves are generally not helpful for evaluating risk prediction models because they do not provide information about the actual risks that the models predict or about the proportion of participants who have high or low risk values . Moreover , when comparing ROC curves for 2 risk prediction models , the models are aligned according to their false-positive rates ( that is , different risk thresholds are applied to the 2 models to achieve the same false-positive rate ) . This is clearly inappropriate . In addition , the area under the ROC curve or c-statistic , a commonly reported summary measure that can be interpreted as the probability that the predicted risk for a participant with an event is higher than that for a participant without an event , has little direct clinical relevance . Clinicians are never asked to compare risks for a pair of patients one who will eventually have the event and one who will not . Neither the ROC curve nor the c-statistic relates to the practical task of predicting risks for clinical decision making . Cook and colleagues ( 1 , 6 ) propose using risk stratification tables to evaluate the incremental value of a new marker , or the benefit of adding a new marker ( for example , C-reactive protein ) , to an established set of risk predictors ( for example , Framingham risk predictors , such as age , diabetes , cholesterol level , smoking , and low-density lipoprotein cholesterol levels ) . In these stratification tables , risks calculated from models with and without the new marker are cross-tabulated . This approach represents a substantial improvement over the use of ROC methodology because it displays the risks calculated by use of the model and the proportions of individuals in the population who are stratified into the risk groups . We will provide an example of this approach and show how information about model calibration , stratification capacity , and classification accuracy can be derived from a risk stratification table and used to assess the added value of a marker for clinical and health care policy decisions . Example Tice and colleagues ( 2 ) published a study that builds and evaluates a model for predicting breast cancer risk by using data from 1095484 women in a prospect i ve cohort and incidence data from the Surveillance , Epidemiology , and End Results data base . Age , race or ethnicity , family history , and history of breast biopsy were used to model risk with a Cox proportional hazard model . The study focused on the benefit of adding breast density information to the model . The hazard ratio for breast density in the multivariate model ( extremely dense vs. almost entirely fat ) was estimated as 4.2 for women younger than age 65 years and 2.2 for women age 65 years or older . This suggests that breast density is strongly associated with disease riskthat is , that breast cancer rates are higher among women with higher breast density . However , it does not describe the value of breast density for helping women make informed clinical decisions , which requires knowledge of the frequency distribution of breast density in the population . To evaluate the added value of breast density , Tice and colleagues defined 5-year breast cancer risk categories as low ( # # lt##1 % ) , low to intermediate ( 1 % to 1.66 % ) , intermediate to high ( 1.67 % to 2.5 % ) , and high ( # # gt##2.5 % ) . The 1.67 % cutoff for intermediate risk was presumably chosen on the basis of recommendations by the American Society of Clinical Oncology ( 8) and the Canadian Task Force on Preventive Health Care ( 9 ) to counsel women with 5-year risks greater than this threshold about considering tamoxifen for breast cancer prevention . Tice and colleagues used a risk stratification table ( Table 1 ) to compare risk prediction models with and without breast density . Table 1 . Five-Year Risks for Breast Cancer as Predicted by Models That Do and Do Not Include Breast Density Calibration Assessing model calibration is an important first step in evaluating any risk prediction model . Good calibration is essential ; it means that the model-predicted probability of an event for a person with specified predictor values is the same as or very close to the proportion of all persons in the population with those same predictor values who experience the event ( 10 ) . With many predictors , and especially with continuous predictors , we can not evaluate calibration at each possible predictor value because there are too few participants with exactly those values . Instead , the st and ard approach is to place persons within categories of predicted risk and to compare the category values with the observed event rates for participants in each category . The calibration of the risk prediction models for breast cancer can be assessed by comparing the proportions of events in the margins of Table 1 with the corresponding row and column labels . For the model without breast density , the proportions of observed events within each risk category are in the far-right Total column and they generally agree OBJECTIVE We investigated the prognostic significance of risk scores and exercise workload with respect to stroke . Background . There are no data on exercise workload combined with European Systematic Coronary Risk Evaluation ( SCORE ) in the prediction of stroke . METHODS  Exercise workload was measured by exercise test with an electrically braked cycle ergometer performed at baseline . The study is based on a r and om population -based sample of 1639 men ( 42 - 60 years ) without history of type 2 diabetes or atherosclerotic cardiovascular disease including coronary heart disease , stroke or claudication . RESULTS During an average follow-up of 16 years , a total of 97 strokes occurred , of which 71 were ischaemic strokes . Independent predictors for all strokes were European SCORE [ for 1 % increment , relative risk ( RR ) : 1.12 , 95 % CI : 1.02 to 1.22 , P=0.017 ) , maximal workload ( for 20 W increment , RR : 0.87 , 95 % CI : 0.80 to 0.95 , P=0.003 ) and body mass index ( for 5 kg m(-2 ) increment , RR : 1.08 , 95 % CI : 1.03 to 1.14 , P=0.004 ) , when adjusted for serum HDL , alcohol consumption , C-reactive protein , family history of coronary heart disease , exercise-induced ST changes and the use of medications for hypertension , dyslipidaemia or aspirin . The risk was 2.54-fold ( 95 % CI : 1.27 - 5.09 , P=0.008 ) for any strokes and 4.43-fold ( 95 % CI 1.69 - 11.78 , 
    Output:
    