Rationale From general practitioners to academic staff, clinicians continue to have difficulties in applying clinical epidemiology in their everyday work. They do not fully understand the logical rules behind the numbers and they do not recognize these rules in their work. We present a new model where the pre-test and the post-test probabilities are converted to log10 of odds, and the likelihood ratio (LR) to its own log10.
Methods Following Bayes’ theorem, adding the log10LR to the log10 pre-test odds gives the log10 post-test odds, which can easily be represented on a logarithmic scale. In addition, by rounding the log10LR to half the unit, we create classes of discriminative power of tests, close to intuitive estimation. This model generates also a user-friendly diagram, adding considerably to the understanding of Bayes’ theorem. We evaluated the effect of the rounding, the current use of the classical model and the acceptability of the new model.
Results Rounding 10 disease characteristics to half the unit gives an absolute error of less than half a unit. After six explanations of Bayes’ theorem, only 6/16 medical students were capable of answering simple questions about predictive value. When asked about weight of disease characteristics, no one of the 50 clinicians mentioned sensitivity, specificity, predictive value or LR. With the new model, more than 80% of trainees found medical decision making easier to understand and recognized the theory in their practice.
Conclusions We conclude that our model of diagnostic clinical epidemiology offers a logical environment for an easy and rapid assessment of the evolution of disease probability with consecutive tests, providing a scientific format for ‘qualitative’ clinical estimations.