IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v12y1992i3p213-221.html
   My bibliography  Save this article

Comparing Methods of Learning Clinical Prediction from Case Simulations

Author

Listed:
  • Thomas G. Tape
  • Jerry Kripal
  • Robert S. Wigton

Abstract

Feedback to physicians about how they use information in making judgments can improve the quality of their judgments, but questions remain about which types of feedback are most effective. The authors conducted a controlled study of feedback in 60 medical students learning to predict the risk of cardiovascular death based on the presence or absence of five risk factors. After a pretest of 40 cases abstracted from patient records, the students worked through 173 computer-simulated cases and a posttest of 40 patient cases. The students received no feedback, probability feedback (correct probability of cardiac death for each case), cognitive feedback (the correct cue weights compared with their own weights derived from the previous set of cases), or both types of feedback. Students who received probability feedback markedly improved both base rate calibration and discrimination. Those who received only cognitive feedback showed no improvement over control on any of the measures of learning. All subjects were highly consistent in their weightings. The superiority of probability feedback differed from previous findings that cognitive feedback was essential for mastery of multiple-cue-probability learning tasks. The information on cue-outcome relationships given by cognitive feedback may be more useful when these relationships are complex and the combining rule is not known, while the precise outcome information provided by probabilistic feedback is more useful when the combining rule is known and the cue- outcome relationships are straightforward. Thus, the optimal method of learning depends on the nature of the task. Key words: risk factors; computer simulations; heart disease. (Med Decis Making 1992;12:213-221)

Suggested Citation

  • Thomas G. Tape & Jerry Kripal & Robert S. Wigton, 1992. "Comparing Methods of Learning Clinical Prediction from Case Simulations," Medical Decision Making, , vol. 12(3), pages 213-221, August.
    • Handle: RePEc:sae:medema:v:12:y:1992:i:3:p:213-221
    DOI: 10.1177/0272989X9201200307
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X9201200307
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X9201200307?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Devon Barrow & Antonija Mitrovic & Jay Holland & Mohammad Ali & Nikolaos Kourentzes, 2024. "Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System," Forecasting, MDPI, vol. 6(1), pages 1-20, March.
    2. Mandeep K. Dhami & Jeryl L. Mumpower, 2018. "Kenneth R. Hammond’s contributions to the study of judgment and decision making," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(1), pages 1-22, January.
    3. repec:cup:judgdm:v:3:y:2008:i:7:p:570-584 is not listed on IDEAS
    4. Jason W. Beckstead, 2008. "Modeling sequential context effects in judgment analysis: A time series approach," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3(7), pages 570-584, October.
    5. repec:cup:judgdm:v:13:y:2018:i:1:p:1-22 is not listed on IDEAS

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:12:y:1992:i:3:p:213-221. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.