
doi: 10.1136/ebm.6.6.164
pmid: 11723805
Understanding how and why Bayes theorem translates pre-test into post-test probabilities is the pons asinorum (Euclid's 5th proposition is known as the “bridge of asses” because many folk got stuck crossing it) of evidence-based medicine (EBM). Because different minds are sparked by different methods, we teach the theorem by using several presentations: 2 × 2 tables, trees, geometric figures, and formulas. A good teacher will have several of these in his or her armamentarium.1 However, having crossed the bridge of understanding, we still must cross the bridge to practice. The basic understanding is essential, but the time pressures of clinical practice require that we abandon the training wheels and move to tools of rapid calculation. No single tool will suit all people and circumstances, so a well-stocked tool box is advisable. In this editorial, I describe 3 methods aimed primarily at everyday practice rather than enhancing understanding: pre-calculated tables or graphs, programmed Bayes calculators, and the Bayes nomogram. Applying Bayes theorem in clinical practice will be quicker if the calculations are already done. For specific common tests, this pre-calculation can be achieved by either tabulating or graphing the post-test probabilities for all plausible pre-test probability values.2 The calculation becomes a simple look-up (provided you have properly organised your information about the test). Ideally, software for critically appraised topics should include …
2700 Medicine, Bayes Theorem, General Medicine, CX, 321099 Clinical Sciences not elsewhere classified, 730305 Diagnostic methods
2700 Medicine, Bayes Theorem, General Medicine, CX, 321099 Clinical Sciences not elsewhere classified, 730305 Diagnostic methods
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
