The article discusses the challenges of evaluating the effectiveness of medical audits in general practice. While trust with family health services is important, it is not sufficient. There is a need for methods to critically assess the benefits of resources spent on audits. Evaluation should consider the work of the medical audit advisory group and use measures that accurately reflect outcomes. Simply counting audits is not enough. The article also explains the concept of predictive values in diagnostic testing. Predictive values depend on the prevalence of a condition. For example, in a liver scan study, with a prevalence of 0.75, the positive predictive value was 0.45 and the negative predictive value was 0.95. These values change depending on the prevalence of the condition. The positive predictive value (PPV) is the probability that a positive test result is a true positive, while the negative predictive value (NPV) is the probability that a negative test result is a true negative. These can be calculated using the formula: PPV = (sensitivity × prevalence) / (sensitivity × prevalence + (1 - specificity) × (1 - prevalence)) NPV = (specificity × (1 - prevalence)) / ((1 - sensitivity) × prevalence + specificity × (1 - prevalence)) The article emphasizes that predictive values are not universal and depend on the prevalence of the condition. It also discusses the importance of likelihood ratios in assessing the usefulness of a test. The likelihood ratio is the ratio of the probability of a test result given the presence of a condition to the probability of the same result given the absence of the condition. The article concludes that while diagnostic tests are important, their interpretation depends on the prevalence of the condition and the context in which they are used. The article also includes a correction to a previous article regarding the association between fat intake and cancer risk.The article discusses the challenges of evaluating the effectiveness of medical audits in general practice. While trust with family health services is important, it is not sufficient. There is a need for methods to critically assess the benefits of resources spent on audits. Evaluation should consider the work of the medical audit advisory group and use measures that accurately reflect outcomes. Simply counting audits is not enough. The article also explains the concept of predictive values in diagnostic testing. Predictive values depend on the prevalence of a condition. For example, in a liver scan study, with a prevalence of 0.75, the positive predictive value was 0.45 and the negative predictive value was 0.95. These values change depending on the prevalence of the condition. The positive predictive value (PPV) is the probability that a positive test result is a true positive, while the negative predictive value (NPV) is the probability that a negative test result is a true negative. These can be calculated using the formula: PPV = (sensitivity × prevalence) / (sensitivity × prevalence + (1 - specificity) × (1 - prevalence)) NPV = (specificity × (1 - prevalence)) / ((1 - sensitivity) × prevalence + specificity × (1 - prevalence)) The article emphasizes that predictive values are not universal and depend on the prevalence of the condition. It also discusses the importance of likelihood ratios in assessing the usefulness of a test. The likelihood ratio is the ratio of the probability of a test result given the presence of a condition to the probability of the same result given the absence of the condition. The article concludes that while diagnostic tests are important, their interpretation depends on the prevalence of the condition and the context in which they are used. The article also includes a correction to a previous article regarding the association between fat intake and cancer risk.