Tips for Teachers of Evidence-based Medicine: Making Sense of Diagnostic Test Results Using Likelihood Ratios

Now is an exciting time to be or become a diagnostician. More diagnostic tests, including portions of the medical interview and physical examination, are being studied rigorously for their accuracy, precision, and usefulness in practice,1,2 and this research is increasingly being systematically reviewed and synthesized.3,4 Diagnosticians are gaining increasing access to this research evidence, raising hope that this knowledge will inform their diagnostic decisions and improve their patients’ clinical outcomes.5 For patients to benefit fully from this accumulating knowledge, the diagnosticians serving them must be able to reason probabilistically, to understand how test results can revise disease probability to confirm or exclude disorders, and to integrate this reasoning with other types of knowledge and diagnostic thinking.6–8 Yet, clinicians encounter several barriers when trying to integrate research evidence into clinical diagnosis.9 Some barriers involve difficulties in understanding and using the quantitative measures of tests’ accuracy and discriminatory power, including sensitivity, specificity, and likelihood ratios (LRs).9,10 We have noticed that LRs are particularly troubling to many learners at first, and we have wondered if this is because of the way they have been taught. Stumbling blocks can arise in several places when learning LRs: the names and formulae themselves can be intimidating; the arithmetic functions can be mystifying when attempted all at once; if two levels of test results are taught first, learners can have difficulty ‘stretching’ to multiple levels; and if disease probability is framed in odds terms (to directly multiply the odds by the likelihood ratio), learners can misunderstand why and how this conversion is done. Other stumbling blocks may occur as well. Other authors have described various approaches to helping clinicians understand LRs.11–16 In this article, we describe two additional approaches to help clinical learners understand how LRs describe the discriminatory power of test results. Whereas we mention other concepts such as pretest and posttest probability, full treatment of those subjects is beyond the scope of this article. These approaches were developed by experienced teachers of evidence-based medicine (EBM) and were refined over years of teaching practice. These tips have also been field-tested to double-check the clarity and practicality of these descriptions, as explained in the introductory article of this series.17 To help the reader envision these teaching approaches, we present sequenced advice for teachers in plain text, coupled with sample words to speak, in italics. These scripts are meant to be interactive, which means that teachers should periodically check in with the learners for their understanding and that teachers should try other ways to explain the ideas if the words we have suggested do not “click.” We present them in order from shorter to longer; however, because these 2 scripts cover the same general content, we encourage teachers to use either or both in an order that best fits their setting and learners.

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