Trapman Episode 11: Waves of Probability
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In this cross-sectional, multi-institutional, Web-based survey study, we sought to determine and compare how third- and fourth-year medical students, internal medicine residents, and academic general internists apply examination findings to their probability assessments and to determine the impact that these findings have on the ordering of diagnostic tests. In , our study recruited participants from three U.
The target population was third-year and fourth-year medical students, internal medicine residents, and academic general internists. No training on Bayesian principles or diagnostic test characteristics was provided to any participant prior to study participation. The study was approved by the institutional review boards at each institution. The survey was written, reviewed, and edited by the study authors and pilot-tested by seven individuals who were not study participants but who represented the three levels of training included in the study.
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The survey began with a series of socio-demographic questions about age, sex, current training or employment status, medical school attended, and, if applicable, date of medical school graduation, name of residency program attended, and date of completion. Next, it asked participants whether they had received formal teaching about the physical examination and about the principles of evidence-based medicine during medical school, residency, or both.
Finally, it presented four cases and asked participants questions about condition probability and diagnostic strategy. We decided to limit the survey to four conditions to avoid overburdening the participants.
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We then included the four highest-ranked conditions—ascites, heart failure, group A beta-hemolytic streptococcal pharyngitis, and acute anterior cruciate ligament ACL tear—in the survey. The key historical items and exam findings included in each case are detailed in Table 1. Participants were instructed that they should assume that the pre-EPs were accurate, that the exam findings were based on examinations conducted by competent clinicians, and that there were no barriers to performing any tests if so needed to arrive at a diagnosis. Pre-EPs were provided because we wanted to start each participant at the same probability, as clinicians vary widely in their initial assessment of condition probability.
Medical Schools, For each of the four conditions, the survey presented two examination scenarios, one consisting of an examination of positive findings, making the condition more likely, and a second examination consisting largely of negative findings, making the condition less likely. For each scenario, the survey asked participants to provide a post-EP, expressed in terms of a whole number percentage ranging from 0 to It also asked participants to select one of three diagnostic options: tell the patient that he or she has the condition in question, tell the patient that further testing is required for diagnosis, or tell the patient that he or she does not have the condition in question.
Participants were not asked to provide specific treatment regimens or to choose specific diagnostic tests. We neither encouraged nor discouraged participants from utilizing outside resources. An unlimited amount of time was given to complete the survey, although, once started, the survey had to be completed in a single sitting. We determined a priori to sample sufficient numbers of participants to allow for meaningful comparison across groups.
After we identified eligible individuals, we sent each an e-mail invitation to complete the survey during an eight-week period in the fall of To increase the response rate, we sent weekly e-mail reminders. We used descriptive statistics to characterize participants in terms of socio-demographic data. This initial probability estimate was then sequentially revised for each historical item using published likelihood ratios in a step-by-step manner by making the posterior probability of the first item the initial probability of the second item and so on. Table 2 details the calculation of unadjusted and adjusted literature-derived post-EPs.
ITL values were utilized instead of raw responses in order to account for the fact that the probability scale behaves differently at the extremes than it does in the middle range. We then used ANOVA and t -tests to determine the degree of concordance among the study groups and between the study-derived values and the literature-derived values. For the diagnostic strategy outcome, we calculated the frequencies of options chosen for each scenario by the total sample and each group. The total sample of participants consisted of students, residents, and academic general internists.
Table 4 provides a summary of the conditional probabilities for each case.
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Findings for all eight scenarios were consistent across groups with only small differences seen the mean absolute difference in post-EP estimates between groups was 2. Table 5 shows the diagnostic options selected for each scenario by the three groups and the total sample. In four of the eight scenarios positive and negative scenarios for ascites, and positive scenarios for streptococcal pharyngitis and acute ACL tear , there were significant differences between groups in terms of the frequencies that each diagnostic option was chosen.
However, in all eight scenarios, the relative ordering of diagnostic options i. For each condition, most participants changed from telling the patient that he or she has the condition in the scenario with positive findings to ordering tests to confirm the diagnosis in the scenario with largely negative findings. In this multi-institutional study examining how medical students, residents, and faculty estimate conditional probabilities and choose diagnostic options for four commonly encountered conditions, we found that these groups tended to similarly undervalue physical examination findings and that they tended to undervalue negative findings to an ever greater extent than they undervalued positive findings.
There are several possible explanations for our findings. First, these results may in fact represent a true undervaluing of the physical examination.
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This could support the previous finding that physicians value diagnostic testing more than findings from the history and physical examination regardless of their diagnostic test characteristics. As a result, our findings could reflect that clinicians are unaware of this evidence and hence, do not apply it to their decision-making. Finally, although the instructions for our study indicated that all examinations were performed by competent clinicians, the participants may have lacked confidence in their own ability to perform physical examinations and may have factored in their own skills when they selected a post-EP or diagnostic option.
Interestingly, we observed only small and clinically insignificant differences between estimates of post-EPs provided by the students, residents, and faculty in our study. This suggests that physicians with more experience in performing examinations do not assign a greater value to examination findings than do trainees. This may also reflect the effect of faculty modeling on residents and students, leading each group to think and perform similarly and to undervalue findings. In the process, it may hinder the teaching of examination skills beyond the basic level.
We also found that large numbers of participants chose to order additional diagnostic testing even when available data suggests that the estimated condition probability is low. Although we did not ask participants to provide the threshold probabilities above and below which they would accept that no further workup is required, our results help to illustrate that physicians differ significantly in their comfort levels in dealing with uncertainty. This is not unexpected, as thresholds could be expected to vary among conditions of different severity and impact.
Responses to the questions about training in our study indicate that while the formal teaching of examination skills is nearly universal in medical schools, it is a much less common in residency programs. With most trainees lacking exposure to a formal curriculum during residency, it is not surprising that a widespread decline in clinical skills has been reported. Although our study had excellent response rates, it had several limitations.
First, although it focused on four common conditions with high face validity i. Second, some might argue that the use of the condition probability outcome is artificial since most physicians probably do not explicitly calculate condition probabilities for their patients.
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We attempted to address this concern by also including a more clinically important outcome—i. Third, in calculating probabilities, we used published likelihood ratios, but these ratios are of varying quality and precision and some are based on research conducted many years ago when the gold standards for diagnosing conditions may have differed from those used today. Also, these published likelihood ratios were developed in specific patient populations and may not be generalizable to other populations. Fourth, when calculating probabilities, we assumed that each item was conditionally independent from the others used.
In order to address the concern that this may not be true, we calculated adjusted probabilities that were more conservative and made the magnitude of our results smaller.
Fifth, because we wanted participants to focus on examination findings rather than history items, we provided pre-EPs for all scenarios. Even though we instructed the participants to view the pre-EPs as accurate, it is possible that some of them ignored this instruction. In this study, trainees and experienced physicians similarly underestimated the impact of examination findings when estimating condition probabilities and, as a consequence, often chose to order additional diagnostic testing to reduce diagnostic uncertainty.
A better understanding of when and how physicians apply examination findings in their assessment of condition probability may provide the foundation for improving the way physicians use these observations in everyday clinical practice. Additional funding for this study was provided by the Shadyside Hospital Foundation. The Shadyside Hospital Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
Thus, in this formula, 41 P x A is the posterior probability and P x is the prior probability. Fortunately, the LRs for many physical examination findings are now available, although the data are of varying quality. As an example, suppose that you are seeing a year-old man who complains of two days of right knee pain that began while he was playing football. He feels like the knee is going to buckle. Next, you examine the knee. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Citing Literature. Volume 14 , Issue 5 September Pages Related Information.
Close Figure Viewer. Browse All Figures Return to Figure. Previous Figure Next Figure. Email or Customer ID. Forgot password? Old Password. Horst, Daniel Gopher, and Samuel Sutton are gratefully acknowledged. Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. The relative contribution of these factors to the ERP waveform was assessed at nine levels of a priori probability from. The EEG was recorded from five midline electrode sites referred to linked mastoids.
Thus, a priori probability and sequential structure appear to be independent determinants of the P complex. Volume 14 , Issue 5. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Psychophysiology Volume 14, Issue 5.