title: The balance beam metaphor: a perspective on clinical diagnosis.
abstract: Understanding the impact of clinical findings in discriminating between possible causes of a patient's presentation is essential in clinical judgment. A balance beam is a natural physical analogue that can accurately represent the combination of several pieces of evidence with varying ability to discriminate between disease hypotheses. Calculation of Bayes' theorem using log(posterior odds) as a function of log(prior odds) and the logarithms of the evidence's likelihood ratios maps onto the physical forces affecting objects placed on a balance beam. We describe the rules governing the functioning of tokens representing clinical findings in the comparison of 2 competing diseases. The likelihood ratios corresponding to positive (LR+) or negative (LR-) observations for each symptom determine the lateral position at which the symptom's token is placed on the beam, using a weight if the finding is present and a helium balloon if it is absent. We discuss how a balance beam could represent concepts of dynamic specificity (due to changes in competitor diseases' probabilities) and dynamic sensitivity (due to class-conditional independence). Utility-based thresholds for acting on a diagnosis could be represented by moving the balance beam's fulcrum. It is suggested that a balance beam can be a useful aid for students learning clinical diagnosis, allowing them to build on existing intuitive understanding to develop an appreciation of how evidence combines to influence degree of belief. The balance beam could also facilitate exploration of the potential impact of available questions or investigations.
authors.affiliation: University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA (RMH, WHB)
authors.forename: Robert M
authors.initials: RM
authors.lastname: Hamm
authors.affiliation: University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA (RMH, WHB)|Howard Live Oak, Inc., Norman, OK, USA (WHB).
authors.forename: William Howard
authors.initials: WH
authors.lastname: Beasley
mesh_terms: D002983:Clinical Competence; D003657:Decision Making; D003933:Diagnosis; D006801:Humans; D007600:Judgment; D016013:Likelihood Functions; D019365:Metaphor; D012680:Sensitivity and Specificity
keywords: Bayes’ theorem; balance beam metaphor; class-conditional independence; medical diagnosis; medical education; spectrum effects; threshold probability