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Video. Concept Teaching Video

Eighteen-minute video shown to participants randomized to the concept trial arm teaching the anchoring and adjusting heuristic as an intuitive equivalent to Bayesian reasoning.

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    1 Comment for this article
    Contradiction between Meaning and Conclusions and Relevance?
    Stefano Olgiati, PhD (Epi and Biostat) | Department of Quantitative Methods, State University of Bergamo, Italy
    Dear Colleagues - a very interesting paper.

    There is a contradiction between the Meaning Section, where you state that: "...significantly improved...", and the Conclusions and Relevance Section, where you state that: "The study showed a modest advantage...".

    Thanks and compliments
    Original Investigation
    Medical Education
    December 20, 2019

    Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis: A Randomized Clinical Trial

    Author Affiliations
    • 1Cardiology Division, Department of Internal Medicine, Eastern Virginia Medical School, Sentara Healthcare, Norfolk
    • 2McMaster Education Research, Innovation and Theory Program, McMaster University, Hamilton, Ontario, Canada
    JAMA Netw Open. 2019;2(12):e1918023. doi:10.1001/jamanetworkopen.2019.18023
    Key Points español 中文 (chinese)

    Question  Can novice clinicians be taught to make more accurate bayesian revisions of diagnostic probabilities using teaching methods involving either explicit conceptual instruction or repeated examples?

    Findings  In this randomized clinical trial of 61 medical students, explicit conceptual instruction on bayesian reasoning and concepts significantly improved the accuracy of posttest probability estimation for novice clinicians, whereas exposure to repeated examples did not. The ability to estimate diagnostic probability was better than expected for all 3 experimental conditions (explicit instruction, repeated examples, and control).

    Meaning  Explicit theoretical instruction significantly improved bayesian revisions of diagnostic probabilities, which has implications for teaching diagnostic reasoning to novice clinicians.


    Importance  Clinicians use probability estimates to make a diagnosis. Teaching students to make more accurate probability estimates could improve the diagnostic process and, ultimately, the quality of medical care.

    Objective  To test whether novice clinicians can be taught to make more accurate bayesian revisions of diagnostic probabilities using teaching methods that apply either explicit conceptual instruction or repeated examples.

    Design, Setting, and Participants  A randomized clinical trial of 2 methods for teaching bayesian updating and diagnostic reasoning was performed. A web-based platform was used for consent, randomization, intervention, and testing of the effect of the intervention. Participants included 61 medical students at McMaster University and Eastern Virginia Medical School recruited from May 1 to September 30, 2018.

    Interventions  Students were randomized to (1) receive explicit conceptual instruction regarding diagnostic testing and bayesian revision (concept group), (2) exposure to repeated examples of cases with feedback regarding posttest probability (experience group), or (3) a control condition with no conceptual instruction or repeated examples.

    Main Outcomes and Measures  Students in all 3 groups were tested on their ability to update the probability of a diagnosis based on either negative or positive test results. Their probability revisions were compared with posttest probability revisions that were calculated using the Bayes rule and known test sensitivity and specificity.

    Results  Of the 61 participants, 22 were assigned to the concept group, 20 to the experience group, and 19 to the control group. Approximate age was 25 years. Two participants were first-year; 37, second-year; 12, third-year; and 10, fourth-year students. Mean (SE) probability estimates of students in the concept group were statistically significantly closer to calculated bayesian probability than the other 2 groups (concept, 0.4%; [0.7%]; experience, 3.5% [0.7%]; control, 4.3% [0.7%]; P < .001). Although statistically significant, the differences between groups were relatively modest, and students in all groups performed better than expected, based on prior reports in the literature.

    Conclusions and Relevance  The study showed a modest advantage for students who received theoretical instruction on bayesian concepts. All participants’ probability estimates were, on average, close to the bayesian calculation. These findings have implications for how to teach diagnostic reasoning to novice clinicians.

    Trial Registration  ClinicalTrials.gov identifier: NCT04130607