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Ruling a diagnosis in or out with “SpPIn” and “SnNOut”: a note of caution

BMJ 2004; 329 doi: (Published 22 July 2004) Cite this as: BMJ 2004;329:209
  1. Daniel Pewsner, senior research fellow1,
  2. Markus Battaglia, senior research fellow1,
  3. Christoph Minder, professor of medical statistics1,
  4. Arthur Marx, senior registrar2,
  5. Heiner C Bucher, professor of clinical epidemiology3,
  6. Matthias Egger (, professor of epidemiology and public health medicine4
  1. 1 Division of Epidemiology and Biostatistics, Department of Social and Preventive Medicine, University of Bern, Switzerland
  2. 2 Department of General Internal Medicine, Inselspital, University of Bern, Switzerland
  3. 3 Basel Institute for Clinical Epidemiology, University Hospitals, Basel, Switzerland
  4. 4 MRC Health Services Research Collaboration, Department of Social Medicine, University of Bristol, Bristol
  1. Correspondence to: M Egger, Department of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, CH-3012 Berne, Switzerland
  • Accepted 20 April 2004


Dr X is back from her annual leave. Dr Y, the locum doctor, reports on the patients he saw during her absence, including a 40 year old teacher who had sprained her right ankle. Returning from a conference, she had stumbled while walking down the stairs with a heavy bag. Examination revealed a moderately swollen lateral right ankle. The patient was able to walk but was clearly in pain. Her breath smelt of alcohol.

Ruling diagnoses in and out with SpPIns and SnNOuts

Dr Y had applied the Ottawa ankle rules—decision rules designed to exclude fractures of the malleolus and the midfoot—and found no bone tenderness.1 He had previously visited the website of a centre for evidence based medicine2 and printed out a list of diagnostic tests that can rule out, or rule in, the condition in question without requiring further investigations.

The probability of disease, given a positive or negative test result (post-test probability), is usually obtained by calculating the likelihood ratio of the test result and using formulas based on Bayes's theorem (see box 1), or a nomogram,3 to convert the estimated probability of the suspected diagnosis before the test result was known (pretest probability) into a post-test probability, which takes the result into account.4 Likelihood ratios indicate how many times more likely a test result is to be expected in a patient with the disease compared with a person free of the disease and thus measure a test's ability to modify pretest probabilities.

David Sackett and others have argued that such calculations are unnecessary when a test is highly sensitive or highly specific.46 In this situation the likelihood ratio of a negative test will generally be very small, and the likelihood ratio of a positive test very large. A negative test will thus rule out, and a positive result …

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