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: Is the human mind inherently unable to reason probabilistically , or is it able to do so only when problems tap into a module for reasoning about natural frequencies ? We suggest an alternative possibility : naive individuals are able to reason probabilistically when they can rely on a representation of subsets of chances or frequencies . We predicted that naive individuals solve conditional probability problems if they can infer conditional probabilities from the subset relations in their representation of the problems , and if the question put to them makes it easy to consider the appropriate subsets . The results of seven studies corroborated these predictions : when the form of the question and the structure of the problem were framed so as to activate intuitive principles based on subset relations , naive individuals solved problems , whether they were stated in terms of probabilities or frequencies . Otherwise , they failed with both sorts of information . The results contravene the frequentist hypothesis and the evolutionary account of probabilistic reasoning Context Educational material s aim ed at improving people 's ability to underst and information about risk are scarce . Contribution In 2 trials , adults with high or low socioeconomic status ( SES ) were r and omly assigned to receive a primer about underst and ing risk or a general health booklet . In both SES groups , adults receiving the primer more often passed a medical data interpretation test than did those receiving the general health booklet . They also expressed greater interest in medical statistics but not greater confidence in interpreting statistics , and most rated the primer helpful or very helpful . Caution s The authors did not examine whether improved data interpretation skills affected decision-making . The Editors People face a bewildering array of medical decisions ( for example , should I be tested for BRCA1 ? Should I be screened for prostate cancer ? Should I have lumpectomy and radiation to treat my breast cancer ? ) . If people are to make informed decisions , they need to underst and risk : What is my chance of staying healthy if I undergo this intervention ? What is my chance of staying healthy if I forgo it ? What is the chance of harm ? Underst and ing risk data entails a set of skills . First , people must be able to work with probabilities and changes in probabilities , the typical measures used to communicate risk information . Next , they need to have a framework on which to organize data . In essence , this means having a sense of what additional information is needed to give meaning to a statement about probability ( conversely , how to know when such additional information is lacking ) . For example , putting a particular risk in context ( your chance of breast cancer is 1 in 8) entails knowing what outcome is being considered ( diagnosis vs. death ) , being clear about the time period ( 5 years vs. lifetime ) , and having some perspective on the magnitude of the probability ( How does the risk for a particular condition compare with other important health risks ? ) . Third , people need to be able to make some basic assessment about the quality of the evidence to know whether they can believe the numbers that they are given . Anecdotal experience and a growing body of literature document the trouble many people have in making sense of risk information ( 14 ) , which is now ubiquitous in health messages . This is not surprising : Much more effort goes into disseminating health information ( for example , drug advertisements , media reports , and decision-making aids ) than into preparing the target audiences to underst and it . To address this gap , we developed a primer to help people develop the basic skills needed to make sense of the medical risk data that surround them . In this paper , we report on 2 r and omized trials that tested the primer . We sought to learn how people would respond to the primer : Would they read it ? Would they value this knowledge ? Most important , would the primer improve patients ' abilities to interpret medical data ? Methods Design Overview To test the effect of the primer on how well people underst and risk , we conducted 2 r and omized trials in distinct population s : people with high and low socioeconomic status ( SES ) . Figure 1 shows an overview of the study design . The Committee for the Protection of Human Subjects at Dartmouth College approved this project , and the survey cover letter was considered to be informed consent ( signed consent was waived ) . Figure 1 . Overview of 2 r and omized trials in distinct population s. SES = socioeconomic status ; VA = Veterans Affairs . Setting and Participants We calculated our sample size under the most conservative conditionswhen the pooled proportion of passing scores was 50 % . We asserted that a 20-percentage point absolute difference in the proportion passing the test in the primer group versus the control group would be clinical ly important . In calculating the sample size requirements , we therefore assumed that the proportion passing would be 50 % in the control group and 70 % in the primer group . When a power of 0.8 and a 2-sided P value of 0.05 were used , 100 patients were required for each study group . Assuming that 10 % of participants would be lost to follow-up , we planned to enroll 110 patients per group in each trial . High Socioeconomic Status Trial To study the effect of the primer in a highly educated and affluent group , we recruited alumni from Dartmouth 's Community Medical School . This 9-lecture series on various health and medical topics is taught by Dartmouth faculty and guests ; is held annually in Hanover , New Hampshire , and Manchester , New Hampshire ; and has a $ 25 registration fee ( 5 ) . For this study , the program organizers mailed recruitment letters to 1138 alumni . A total of 334 people who responded to the letter were eligible ( that is , they met the age criterion of 35 to 79 years , spoke English , and had attended the Community Medical School in the past ) and were subsequently r and omly assigned . Ninety-six percent ( n= 322 ) returned a completed survey ( completion rates were not significantly different between the primer and control groups [ 95 % vs. 98 % , respectively ] ) ( P= 0.192 ) . Low Socioeconomic Status Trial To study the effect of the primer in a sample with lower income and less formal education , we recruited veterans and their families at the White River Junction Veterans Affairs ( VA ) Medical Center , White River Junction , Vermont , by posting study advertisements in waiting areas of the outpatient clinic . A total of 221 people who responded to the advertisement were eligible ( that is , they met the age criteria of 35 to 79 years , spoke English , and were a veteran or the family member of a veteran who was enrolled in a VA clinic ) and were subsequently r and omly assigned . Ninety percent ( n= 200 ) returned completed surveys ( completion rates were significantly lower in the primer group than in the control group [ 85 % vs. 96 % , respectively ] ) ( P= 0.005 ) . R and omization and Interventions The letter and the advertisement asked people to participate in a research study to learn how to better give people health information . We did not mention our interest in enhancing quantitative skills . People who responded to the recruitment letter ( high SES group ) or to the advertisement ( low SES group ) were first interviewed to confirm eligibility . We only accepted 1 participant per household . Within each trial , we assigned participants on an individual basis to receive either the primer or control booklet . A list of r and om numbers ( created by using a r and om-number generator ) was given to a research assistant and was used to determine assignments ( r and omization was not stratified or blocked within each trial ) . The research assistant had access to participants ' characteristics . The investigators did not have access to the assignment list , and the deidentified code was only revealed after recruitment and completion of data collection . The appropriate booklet and a survey were mailed or given in person to participants . The survey included the major outcomes that will be described in the next section . To make the survey seem relevant to the control group , it also included additional questions about facts presented in the health booklet ( we did not analyze responses to these questions ) . All participants were asked to read the booklet and return the completed survey within 2 weeks using the self-addressed stamped envelope included with the survey . Reminder letters were sent to nonresponders . Participants who returned surveys were given their choice of a $ 25 gift certificate to a local bakery , restaurant , bookstore , or large retail store . We recruited participants from October 2004 through August 2005 . Primer Group The participants in the primer group received the booklet entitled Know Your Chances : Underst and ing Health Statistics . The goal of the primer is to teach people how to underst and risk messages and health statistics . The first part of the primer teaches people how to underst and disease risk by using the example of colon cancer . The second part focuses on how to underst and the benefits and harms of interventions by using a Zocor direct-to-consumer advertisement for secondary heart disease prevention . Figure 2 shows the summary of main concepts that were taught , which is excerpted from the booklet . The contents of the primer and many of the examples have been developed and revised over years of teaching and through focus groups with people across a diverse socioeconomic spectrum . Figure 2 . Key concepts taught in the primer , excerpted from the final pages . Because many people are intimi date d by numbers and statistics , we worked hard to make the primer inviting and nonthreatening by liberal use of cartoons and figures ; by working through examples , separating the most technical material into optional learn more boxes ; and by providing readers with quizzes ( with answers ) to assess their mastery of the material as they read the primer . Figure 3 shows sample pages from the primer . Most of the primer is written at the eighth- grade or lower reading level ( 6 ) and is a color document approximately 80 pages in length . Figure 3 . Excerpts of primer . Control Group The control group received a 70-page booklet entitled The Pocket Guide for Good Health for Adults , which is published by the U.S. Department of Health and Human Services Agency for Health Care Research and Quality ( 7 ) . We chose this booklet because the length is similar to that of the primer , it is written at a similar reading level ( eighth grade or lower ) ( 6 ) , and it contains general information about risk and reducing risk by following recommended prevention or screening activities . However , it does not include training on how to interpret quantitative information . Measurement and Outcomes All outcome measures were assessed in the survey . Primary Outcome The primary outcome was a measure of participants ' abilities to interpret medical statistics . To measure this outcome , we developed the 18-item data interpretation test during the same time Patients are increasingly being exposed to quantitative information about risks for disease and benefits of treatment . Many authors [ 1 - 6 ] believe that the use of such information is an important component of informed decision making ; others cl aim that only patients given such information can make truly informed choices . This position is reflected in the recent decision of the National Institutes of Health Consensus Panel not to make a recommendation about screening mammography for women aged 40 to 49 years but to advocate that these women make their own decisions about screening on the basis of their personal evaluation of risks and benefits [ 7 ] . This strategy , however , is based on the assumption that patients underst and quantitative information . Research in this area has largely focused on how simple changes in the format of numerical information can influence choices , a concept referred to as framing [ 8 - 20 ] . But just as it is premature to worry about wording before knowing whether patients can read , it may be premature to worry about framing before knowing whether patients can underst and and manipulate numbers . In fact , evidence suggests that many persons do not work well with numbers [ 21 , 22 ] . It is likely that quantitative information is only meaningful to the extent that patients have some facility with basic probability and numerical concepts , a construct called numeracy . To learn more about the role of numeracy in communicating information about risk , we studied women 's comprehension of messages about mammography . Our goal was to underst and how numeracy affects women 's ability to gauge the benefit of mammography after receiving quantitative information . We provided women with typical risk reduction expressions about mammography , framed in relative and absolute terms , to study how well they could make use of such information . We hypothesized that the ability to use quantitative risk information would be related to the level of numeracy . Methods Study Design and Sample In December 1995 , we drew a simple r and om sample of 500 women from a registry of female veterans maintained at the Department of Veterans Affairs Medical Center , White River Junction , Vermont . These women were mailed one of four question naires , which differed only in how the same information on average risk reduction with mammography was presented . Reminder letters were sent to nonrespondents after 2 weeks , and new copies of the question naire were mailed after 4 weeks . The study was closed in February 1996 . Because of errors in the registry , 26 entries in our sample were not usable ( 10 of the listed persons were deceased , and 16 either were not female veterans or were listed twice ) . Thus , our possible respondent pool totaled 474 , of which 302 ( 64 % ) returned the question naire . The returned surveys sometimes contained unanswered questions . Such blanks may represent information about the respondent 's abilities , or they may be a marker of an unmotivated respondent or one who has become fatigued from the survey burden . To remove those who were unmotivated or fatigued , we required completion of four of the five questions on the last page of the survey for inclusion in the analyses . Only 15 women did not meet this criterion . Thus , our final sample comprised 287 women , for a 61 % completion rate . Unanswered numeracy questions were considered wrong answers . Restricting the sample to women who answered these questions yielded results similar to those seen with the entire sample . Question naire Design Assessment of Numeracy Numeracy was assessed with three questions and was scored as the total number of correct responses . The first question assessed basic familiarity with probability : Imagine that we flip a fair coin 1,000 times . What is your best guess about how many times the coin would come up heads in 1,000 flips ? _ _ _ _ times out of 1,000 . The second question asked respondents to convert a percentage ( 1 % ) to a proportion ( 10 in 1000 ) : In the BIG BUCKS LOTTERY , the chance of winning a $ 10 prize is 1 % . What is your best guess about how many people would win a $ 10 prize if 1000 people each buy a single ticket to BIG BUCKS?____person(s ) out of 1,000 . The third question reversed this task , asking the respondent to convert a proportion ( 1 in 1000 ) to a percentage ( 0.1 % ) : In ACME PUB 
    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: Is the human mind inherently unable to reason probabilistically , or is it able to do so only when problems tap into a module for reasoning about natural frequencies ? We suggest an alternative possibility : naive individuals are able to reason probabilistically when they can rely on a representation of subsets of chances or frequencies . We predicted that naive individuals solve conditional probability problems if they can infer conditional probabilities from the subset relations in their representation of the problems , and if the question put to them makes it easy to consider the appropriate subsets . The results of seven studies corroborated these predictions : when the form of the question and the structure of the problem were framed so as to activate intuitive principles based on subset relations , naive individuals solved problems , whether they were stated in terms of probabilities or frequencies . Otherwise , they failed with both sorts of information . The results contravene the frequentist hypothesis and the evolutionary account of probabilistic reasoning Context Educational material s aim ed at improving people 's ability to underst and information about risk are scarce . Contribution In 2 trials , adults with high or low socioeconomic status ( SES ) were r and omly assigned to receive a primer about underst and ing risk or a general health booklet . In both SES groups , adults receiving the primer more often passed a medical data interpretation test than did those receiving the general health booklet . They also expressed greater interest in medical statistics but not greater confidence in interpreting statistics , and most rated the primer helpful or very helpful . Caution s The authors did not examine whether improved data interpretation skills affected decision-making . The Editors People face a bewildering array of medical decisions ( for example , should I be tested for BRCA1 ? Should I be screened for prostate cancer ? Should I have lumpectomy and radiation to treat my breast cancer ? ) . If people are to make informed decisions , they need to underst and risk : What is my chance of staying healthy if I undergo this intervention ? What is my chance of staying healthy if I forgo it ? What is the chance of harm ? Underst and ing risk data entails a set of skills . First , people must be able to work with probabilities and changes in probabilities , the typical measures used to communicate risk information . Next , they need to have a framework on which to organize data . In essence , this means having a sense of what additional information is needed to give meaning to a statement about probability ( conversely , how to know when such additional information is lacking ) . For example , putting a particular risk in context ( your chance of breast cancer is 1 in 8) entails knowing what outcome is being considered ( diagnosis vs. death ) , being clear about the time period ( 5 years vs. lifetime ) , and having some perspective on the magnitude of the probability ( How does the risk for a particular condition compare with other important health risks ? ) . Third , people need to be able to make some basic assessment about the quality of the evidence to know whether they can believe the numbers that they are given . Anecdotal experience and a growing body of literature document the trouble many people have in making sense of risk information ( 14 ) , which is now ubiquitous in health messages . This is not surprising : Much more effort goes into disseminating health information ( for example , drug advertisements , media reports , and decision-making aids ) than into preparing the target audiences to underst and it . To address this gap , we developed a primer to help people develop the basic skills needed to make sense of the medical risk data that surround them . In this paper , we report on 2 r and omized trials that tested the primer . We sought to learn how people would respond to the primer : Would they read it ? Would they value this knowledge ? Most important , would the primer improve patients ' abilities to interpret medical data ? Methods Design Overview To test the effect of the primer on how well people underst and risk , we conducted 2 r and omized trials in distinct population s : people with high and low socioeconomic status ( SES ) . Figure 1 shows an overview of the study design . The Committee for the Protection of Human Subjects at Dartmouth College approved this project , and the survey cover letter was considered to be informed consent ( signed consent was waived ) . Figure 1 . Overview of 2 r and omized trials in distinct population s. SES = socioeconomic status ; VA = Veterans Affairs . Setting and Participants We calculated our sample size under the most conservative conditionswhen the pooled proportion of passing scores was 50 % . We asserted that a 20-percentage point absolute difference in the proportion passing the test in the primer group versus the control group would be clinical ly important . In calculating the sample size requirements , we therefore assumed that the proportion passing would be 50 % in the control group and 70 % in the primer group . When a power of 0.8 and a 2-sided P value of 0.05 were used , 100 patients were required for each study group . Assuming that 10 % of participants would be lost to follow-up , we planned to enroll 110 patients per group in each trial . High Socioeconomic Status Trial To study the effect of the primer in a highly educated and affluent group , we recruited alumni from Dartmouth 's Community Medical School . This 9-lecture series on various health and medical topics is taught by Dartmouth faculty and guests ; is held annually in Hanover , New Hampshire , and Manchester , New Hampshire ; and has a $ 25 registration fee ( 5 ) . For this study , the program organizers mailed recruitment letters to 1138 alumni . A total of 334 people who responded to the letter were eligible ( that is , they met the age criterion of 35 to 79 years , spoke English , and had attended the Community Medical School in the past ) and were subsequently r and omly assigned . Ninety-six percent ( n= 322 ) returned a completed survey ( completion rates were not significantly different between the primer and control groups [ 95 % vs. 98 % , respectively ] ) ( P= 0.192 ) . Low Socioeconomic Status Trial To study the effect of the primer in a sample with lower income and less formal education , we recruited veterans and their families at the White River Junction Veterans Affairs ( VA ) Medical Center , White River Junction , Vermont , by posting study advertisements in waiting areas of the outpatient clinic . A total of 221 people who responded to the advertisement were eligible ( that is , they met the age criteria of 35 to 79 years , spoke English , and were a veteran or the family member of a veteran who was enrolled in a VA clinic ) and were subsequently r and omly assigned . Ninety percent ( n= 200 ) returned completed surveys ( completion rates were significantly lower in the primer group than in the control group [ 85 % vs. 96 % , respectively ] ) ( P= 0.005 ) . R and omization and Interventions The letter and the advertisement asked people to participate in a research study to learn how to better give people health information . We did not mention our interest in enhancing quantitative skills . People who responded to the recruitment letter ( high SES group ) or to the advertisement ( low SES group ) were first interviewed to confirm eligibility . We only accepted 1 participant per household . Within each trial , we assigned participants on an individual basis to receive either the primer or control booklet . A list of r and om numbers ( created by using a r and om-number generator ) was given to a research assistant and was used to determine assignments ( r and omization was not stratified or blocked within each trial ) . The research assistant had access to participants ' characteristics . The investigators did not have access to the assignment list , and the deidentified code was only revealed after recruitment and completion of data collection . The appropriate booklet and a survey were mailed or given in person to participants . The survey included the major outcomes that will be described in the next section . To make the survey seem relevant to the control group , it also included additional questions about facts presented in the health booklet ( we did not analyze responses to these questions ) . All participants were asked to read the booklet and return the completed survey within 2 weeks using the self-addressed stamped envelope included with the survey . Reminder letters were sent to nonresponders . Participants who returned surveys were given their choice of a $ 25 gift certificate to a local bakery , restaurant , bookstore , or large retail store . We recruited participants from October 2004 through August 2005 . Primer Group The participants in the primer group received the booklet entitled Know Your Chances : Underst and ing Health Statistics . The goal of the primer is to teach people how to underst and risk messages and health statistics . The first part of the primer teaches people how to underst and disease risk by using the example of colon cancer . The second part focuses on how to underst and the benefits and harms of interventions by using a Zocor direct-to-consumer advertisement for secondary heart disease prevention . Figure 2 shows the summary of main concepts that were taught , which is excerpted from the booklet . The contents of the primer and many of the examples have been developed and revised over years of teaching and through focus groups with people across a diverse socioeconomic spectrum . Figure 2 . Key concepts taught in the primer , excerpted from the final pages . Because many people are intimi date d by numbers and statistics , we worked hard to make the primer inviting and nonthreatening by liberal use of cartoons and figures ; by working through examples , separating the most technical material into optional learn more boxes ; and by providing readers with quizzes ( with answers ) to assess their mastery of the material as they read the primer . Figure 3 shows sample pages from the primer . Most of the primer is written at the eighth- grade or lower reading level ( 6 ) and is a color document approximately 80 pages in length . Figure 3 . Excerpts of primer . Control Group The control group received a 70-page booklet entitled The Pocket Guide for Good Health for Adults , which is published by the U.S. Department of Health and Human Services Agency for Health Care Research and Quality ( 7 ) . We chose this booklet because the length is similar to that of the primer , it is written at a similar reading level ( eighth grade or lower ) ( 6 ) , and it contains general information about risk and reducing risk by following recommended prevention or screening activities . However , it does not include training on how to interpret quantitative information . Measurement and Outcomes All outcome measures were assessed in the survey . Primary Outcome The primary outcome was a measure of participants ' abilities to interpret medical statistics . To measure this outcome , we developed the 18-item data interpretation test during the same time Patients are increasingly being exposed to quantitative information about risks for disease and benefits of treatment . Many authors [ 1 - 6 ] believe that the use of such information is an important component of informed decision making ; others cl aim that only patients given such information can make truly informed choices . This position is reflected in the recent decision of the National Institutes of Health Consensus Panel not to make a recommendation about screening mammography for women aged 40 to 49 years but to advocate that these women make their own decisions about screening on the basis of their personal evaluation of risks and benefits [ 7 ] . This strategy , however , is based on the assumption that patients underst and quantitative information . Research in this area has largely focused on how simple changes in the format of numerical information can influence choices , a concept referred to as framing [ 8 - 20 ] . But just as it is premature to worry about wording before knowing whether patients can read , it may be premature to worry about framing before knowing whether patients can underst and and manipulate numbers . In fact , evidence suggests that many persons do not work well with numbers [ 21 , 22 ] . It is likely that quantitative information is only meaningful to the extent that patients have some facility with basic probability and numerical concepts , a construct called numeracy . To learn more about the role of numeracy in communicating information about risk , we studied women 's comprehension of messages about mammography . Our goal was to underst and how numeracy affects women 's ability to gauge the benefit of mammography after receiving quantitative information . We provided women with typical risk reduction expressions about mammography , framed in relative and absolute terms , to study how well they could make use of such information . We hypothesized that the ability to use quantitative risk information would be related to the level of numeracy . Methods Study Design and Sample In December 1995 , we drew a simple r and om sample of 500 women from a registry of female veterans maintained at the Department of Veterans Affairs Medical Center , White River Junction , Vermont . These women were mailed one of four question naires , which differed only in how the same information on average risk reduction with mammography was presented . Reminder letters were sent to nonrespondents after 2 weeks , and new copies of the question naire were mailed after 4 weeks . The study was closed in February 1996 . Because of errors in the registry , 26 entries in our sample were not usable ( 10 of the listed persons were deceased , and 16 either were not female veterans or were listed twice ) . Thus , our possible respondent pool totaled 474 , of which 302 ( 64 % ) returned the question naire . The returned surveys sometimes contained unanswered questions . Such blanks may represent information about the respondent 's abilities , or they may be a marker of an unmotivated respondent or one who has become fatigued from the survey burden . To remove those who were unmotivated or fatigued , we required completion of four of the five questions on the last page of the survey for inclusion in the analyses . Only 15 women did not meet this criterion . Thus , our final sample comprised 287 women , for a 61 % completion rate . Unanswered numeracy questions were considered wrong answers . Restricting the sample to women who answered these questions yielded results similar to those seen with the entire sample . Question naire Design Assessment of Numeracy Numeracy was assessed with three questions and was scored as the total number of correct responses . The first question assessed basic familiarity with probability : Imagine that we flip a fair coin 1,000 times . What is your best guess about how many times the coin would come up heads in 1,000 flips ? _ _ _ _ times out of 1,000 . The second question asked respondents to convert a percentage ( 1 % ) to a proportion ( 10 in 1000 ) : In the BIG BUCKS LOTTERY , the chance of winning a $ 10 prize is 1 % . What is your best guess about how many people would win a $ 10 prize if 1000 people each buy a single ticket to BIG BUCKS?____person(s ) out of 1,000 . The third question reversed this task , asking the respondent to convert a proportion ( 1 in 1000 ) to a percentage ( 0.1 % ) : In ACME PUB 
    Output:
    