***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: Objective : To determine whether physicians ’ post-test probability estimates are influenced by receiving test characteristics and impact their subsequent clinical decisions . Design : Question naire based r and omised controlled trial . Setting : Mailed survey with a vignette describing an infant whose pretest likelihood of pertussis was 30 % and direct fluorescent-antibody ( DFA ) test was negative for pertussis . Subjects : Nationally representative sample of US paediatricians ( n = 1502 ) . Interventions : R and om receipt of no additional information ( controls ) , the DFA ’s sensitivity and specificity ( TC group ) or the test ’s sensitivity and specificity with their definitions ( TCD group ) . Main outcome measures : Estimated post-test probability ( PTP ) of pertussis , PTP of 0.50 , “ nearly correct ” PTP ( ±5 % ) , intended erythromycin management and intended hospital disposition . Analyses : χ2 and t tests . Results : Despite the negative DFA result , 67 % of the 635 ( 49.7 % ) participants who responded estimated a PTP higher than the pretest probability of 30 % ; the overall mean estimated PTP was 0.41 ( SD 0.26 ) ( correct answer : 0.18 ) . The TCD group ’s mean PTP was significantly higher than controls ’ mean PTP ( 0.45 vs 0.38 , p<0.001 ) , while the TC and control groups ’ mean PTP did not differ significantly ( 0.41 vs 0.38 , p = 0.16 ) . With decision support significantly more TC and TCD participants compared to controls estimated the PTP as 0.50 ( 38 % vs 17 % , p<0.001 ; 41 % vs 17 % , p<0.001 , respectively ) and also estimated a nearly correct PTP more often ( 20 % vs 13 % , p = 0.06 ; 19 % vs 13 % , p = 0.08 , respectively ) . The mean PTP of participants intending to discontinue erythromycin therapy or discharge the patient home was significantly lower than that of participants who intended continuing erythromycin or hospitalisation ( 0.20 vs 0.43 , p<0.001 ; 0.40 vs 0.49 , p = 0.005 , respectively ) . Conclusions : Paediatricians differed in their response to information about test characteristics . For many , it increased errors in estimating post-test probability ; for others , it reduced errors . Estimated post-test probability was logically associated with intended clinical management Abstract Objective : To assess the extent to which different forms of summarising diagnostic test information influence general practitioners ' ability to estimate disease probabilities . Design : Controlled question naire study . Setting : Three Swiss conferences in continuous medical education . Participants : 263 general practitioners . Intervention : Question naire with multiple choice questions about terms of test accuracy and a clinical vignette with the results of a diagnostic test described in three different ways ( test result only , test result plus test sensitivity and specificity , test result plus the positive likelihood ratio presented in plain language ) . Main outcome measures : Doctors ' knowledge and application of terms of test accuracy and estimation of disease probability in the clinical vignette . Results : The correct definitions for sensitivity and predictive value were chosen by 76 % and 61 % of the doctors respectively , but only 22 % chose the correct answer for the post-test probability of a positive screening test . In the clinical vignette doctors given the test result only overestimated its diagnostic value ( median attributed likelihood ratio (aLR)=9.0 , against 2.54 reported in the literature ) . Providing the scan 's sensitivity and specificity reduced the overestimation ( median aLR=6.0 ) but to a lesser extent than simple wording of the likelihood ratio ( median aLR=3.0 ) . Conclusion : Most general practitioners recognised the correct definitions for sensitivity and positive predictive value but did not apply them correctly . Conveying test accuracy information in simple , non-technical language improved their ability to estimate disease probabilities accurately . What is already known on this topic Many doctors confuse the sensitivity of clinical tests and their positive predictive value Doctors tend to overestimate information derived from such tests and underestimate information from a patient 's clinical history Most primary research on diagnostic accuracy is reported using sensitivity and specificity or likelihood ratios What this study adds In a cohort of experienced Swiss general practitioners most were unable to interpret correctly numerical information on the diagnostic accuracy of a screening test When presented with a positive result alone they grossly overestimated its value Adding information on the test 's sensitivity and specificity moderated these overestimates , and expressing the same numerical information as a positive likelihood ratio in simple , non-technical language brought the estimates still closer to their true Abstract Objective To investigate the accuracy of interpretation of  probabilistic screening information  by different stakeholder groups and whether presentation as frequencies improves accuracy . Design Between participants experimental design ; participants responded to screening information embedded in a scenario . Setting Regional maternity service and national conferences and training days . Participants 43 pregnant women attending their first antenatal appointment in a regional maternity service ; 40 companions accompanying the women to their appointments ; 42 midwives ; 41 obstetricians . Participation rates were 56 % , 48 % , 89 % , and 71 % respectively . Measures Participants estimated the probability that a positive screening test result meant that a baby actually had Down 's syndrome on the basis of all the relevant information , which was presented in a scenario . They were r and omly assigned to scenarios that presented the information in percentage ( n = 86 ) or frequency ( n = 83 ) format . They also gave basic demographic information and rated their confidence in their estimate . Results Most responses ( 86 % ) were incorrect . Obstetricians gave significantly more correct answers ( although still only 43 % ) than either midwives ( 0 % ) or pregnant women ( 9 % ) . Overall , the proportion of correct answers was higher for presentation as frequencies ( 24 % ) than for presentation as percentages ( 6 % ) , but further analysis showed that this difference occurred only in responses from obstetricians . Many health professionals were confident in their incorrect responses . Conclusions Most stakeholders in pregnancy screening draw incorrect inferences from probabilistic information , and health professionals need to be aware of the difficulties that both they and their patients have with such information . Moreover , they should be aware that different people make different mistakes and that ways of conveying information that help some people will not help others BACKGROUND Hospital pharmacists participate in activities that may be considered diagnostic . Two reasoning approaches to diagnosis have been described : non-analytic and analytic . Of the 6 analytic traditions , the probabilistic tradition has been shown to improve diagnostic accuracy and reduce unnecessary testing . To the authors ' knowledge , pharmacists ' attitudes toward having a diagnostic role and their diagnostic knowledge and skills have never been studied . OBJECTIVES To describe pharmacists ' attitudes toward the role of diagnosis in pharmacotherapeutic problem-solving and to characterize the extent of pharmacists ' knowledge and skills related to diagnostic literacy . METHODS Pharmacists working within Lower Mainl and Pharmacy Services ( British Columbia ) who spent at least 33 % of their time in direct patient care were invited to participate in a prospect i ve  observational survey . The survey sought information about demographic characteristics and attitudes toward diagnosis . Diagnostic knowledge and skills were tested by means of 3 case scenarios . The analysis included simple descriptive statistics and inferential statistics to evaluate relationships between responses and experience and training . RESULTS Of 266 pharmacists invited to participate , 94 responded . The attitudes section of the survey was completed by 90 pharmacists ; of these , 80 ( 89 % ) agreed with the definition of " diagnosis " proposed in the survey , and 83 ( 92 % ) agreed that it is important for pharmacists to have diagnosis-related skills . Respondents preferred an analytic to a non-analytic approach to diagnostic decision-making . The probabilistic tradition was not the preferred method in any of the 3 cases . In evaluating 5 clinical scenarios that might require diagnostic skills , on average 84 % of respondents agreed that they should be involved in assessing such problems . Respondents ' knowledge of and ability to apply probabilistic diagnostic tools were highest for test sensitivity ( average of 61 % of respondents with the correct answers ) and lower for test specificity ( average of 48 % with correct answers ) and likelihood ratios ( average of 39 % with correct answers ) . CONCLUSIONS Respondents to this survey strongly believed that diagnostic skills were important for solving drug-related problems , but they demonstrated low levels of knowledge and ability to apply concepts of probabilistic diagnostic reasoning . Opportunities to exp and pharmacists ' knowledge of diagnostic reasoning exist , and the findings reported here indicate that pharmacists would consider such professional development valuable Context There are several ways to present information about the accuracy of diagnostic tests . Contribution In this vignette-based study , 183 physicians read 6 scenarios that included patients ' presenting symptoms , risk profiles , and test results . Physicians were r and omly assigned to view information about test accuracy as sensitivity and specificity , a likelihood ratio , or an inexact graphic of the likelihood ratio . In general , the groups estimated similar post-test probabilities of disease regardless of how diagnostic accuracy was presented . Implication s This preliminary work suggests some physicians may interpret sensitivity and specificity and likelihood ratio information in a similar manner . The Editors Diagnostic tests are used to reduce uncertainty about the presence or absence of an illness . The informativeness of a diagnostic test , usually expressed as its sensitivity and specificity or as a likelihood ratio , determines how much the illness probability changes from the pretest to the post-test situation . The change in illness probability can be calculated using the Bayes theorem , which combines the illness probability before testing with the information derived from the test ( 1 ) . Few physicians use this formal approach to weigh the informativeness of diagnostic test results ( 2 ) and , perhaps as a result , make mistakes when asked to use it ( 3 , 4 ) . Many investigators advocate the use of likelihood ratios ( 5 - 8 ) . However , the effect of different presentation formats for diagnostic accuracy on post-test probability estimates has not been rigorously tested . We conducted a r and omized study to see if physicians ' estimates about illness probability more closely reflect estimates determined by the Bayes theorem when diagnostic information is presented as sensitivity and specificity estimates , a likelihood ratio , or an inexact numerical graphic . Methods We r and omly assigned experienced specialists in family and internal medicine attending a continuing medical education conference to complete 1 of 3 self-administered question naires that presented vignettes representative of scenarios commonly encountered early in the diagnostic work-up . The vignettes differed only in how diagnostic test accuracy was presented . The question naires were sealed within 576 envelopes that had been r and omized in 32 blocks of 18 to avoid order effects ( 6 different vignette orders for each of the 3 test accuracy presentation formats ) . We distributed the envelopes before participants entered the lecture hall so that we could not predict which participant received which envelope , thus concealing the r and omization . The moderator of the lecture ( which was unrelated to material presented in the vignettes ) advised those present to complete the question naire during small breaks or at the end of the lecture . We collected 183 question naires 15 minutes after the lecture and excluded 5 question naires that were sent to us later . The question naires asked participants to assess 6 clinical patient profiles and to estimate the probability of illness , specifying a percentage between 0 % and 100 % , for each scenario . With the exception of the chronic obstructive pulmonary disease vignette , all patient vignettes consisted of a description of presenting symptoms , additional clinical facts , and a risk profile ( Appendix Table ) . In the chronic obstructive pulmonary disease vignette , no presenting symptom but only the risk profile was given . For each vignette , we then gave the result of a diagnostic test commonly used in clinical practice and the test 's accuracy derived from the literature , presented as sensitivities and specificities ( group 1 ) , as a positive or negative likelihood ratio ( group 2 ) , or as an inexact numerical graphical format ( group 3 ) . Using the test result and its diagnostic accuracy , participants were then invited to estimate the post-test probability . As shown in the Appendix Table , we varied the informativeness of the diagnostic tests for both negative and positive results by presenting examples of weak ( vignettes 1 and 4 ) , intermediate ( vignettes 2 and 5 ) , and strong ( vignettes 3 and 6 ) test accuracies . Sensitivities and specificities were given as percentages to group 1 . In group 2 , the positive or negative likelihood ratios were presented as a numerical value and described in nontechnical language . For example , the description of the  d-dimer test  's positive likelihood ratio of 1.7 in patients with high probability of pulmonary embolism ( 9 ) was additionally described as in patients with pulmonary embolism , a positive test result is found almost twice as often as in patients without pulmonary embolism . For group 3 , we depicted an inexact numerical presentation with a simple graphic of 5 circles ( ) . We darkened an increasing number of circles to correspond with increasing positive likelihood ratios or decreasing negative likelihood ratios ( 10 ) . For example , positive likelihood ratios for 1 to 2.4 and for greater than 10 were presented as and , respectively , while negative likelihood ratios for 1 to 0.41 and for less than 0.1 were presented as and , respectively . Experienced clinicians design ed and review ed the vignettes . Statistical Analysis We calculated medians and interquartile ranges ( 25th to 75th percentile ) for pretest and post-test probabilities . On the basis of each individual pretest probability estimate , we calculated a corresponding Bayes-based post-test probability by plugging the likelihood ratios corresponding to each clinical vignette into the Bayes theorem . We calculated the differences between Bayes-based post-test probabilities  and the  estimated post-test probabilities . Because these differences were distributed normally , we used 2- sample t-tests to compare the differences between groups 1 ( sensitivity and specificity ) and 2 ( likelihood ratio ) , as well 
    Output:
    
5 studies found that presenting natural frequencies rather than probabilities improved post-test probability estimation and speed of calculations .
CONCLUSIONS Commonly used measures of test accuracy are poorly understood by health professionals .
Reporting test accuracy using natural frequencies and visual aids may facilitate improved underst and ing and better estimation of the post-test probability of disease