In many ways much of the art of medicine boils down to playing the percentages and predicting outcomes. For example, when clinicians take a history from a patient they ask the questions that they think are the most likely to provide them with the information they need to make a diagnosis. They might then order the tests that they think are the most likely to support or refute their various differential diagnoses. With each new piece of the puzzle some hypotheses will become more likely and others less likely. At the end of the process the clinician will decide which treatment is likely to result in the most favourable outcome for the patient, based on the information they have obtained.

Given that the above process is the underlying principle of clinical practice, and bearing in mind the ever increasing time constraints imposed on people, it is unsurprising that a great deal of work has been done to help clinicians and patients make decisions. This work is referred to by many names: prediction rules, probability assessments, prediction models, decision rules, risk scores, etc. All describe the combination of multiple predictors, such as patient characteristics and investigation results, to estimate the probability of a certain outcome or to identify which intervention is most likely to be effective.1 2 Predictors are identified by “data mining”—the process of selecting, exploring, and modelling large amounts of data in order to discover unknown patterns or relations.3

Ideally, a reliable predictive factor or model would combine both a high …