Making a diagnosis in primary care: symptoms and context.
Year: 2004
Type: article
Abstract: DIAGNOSIS can be difficult. It is especially difficult in primary care where serious diseases, such as cancer or heart disease, are rare, there is a greater reliance on symptoms, and general practitioners (GPs) are constantly bombarded with guidelines that ignore the primary care context.
The positive predictive value (that is, the probability that the disease is present if the patient has a symptom or a positive test result) often makes the most intuitive sense to clinicians and yet is a constant source of misunderstanding between GPs and our secondary care colleagues. It is imperative to be aware that the predictive value is affected by the prevalence: as the prevalence falls, the number of false positives tends to increase, resulting in a lowering of the positive predictive value.1 The effect of prevalence can also be readily understood in relation to the odds ratio version of Bayes' theorem: posterior odds = likelihood ratio × prior odds (see Box 1 for definitions).
### Box 1. Bayesian terminology.
Likelihood ratio
Prior (pre-test) odds
Posterior (post-test) odds
Thus, in a low prevalence population like primary care the posterior odds of disease will be lower than in a higher prevalence hospital-based population, even if the same clinical features, such as symptoms …
Source: Europe PMC (PubMed Central)
Author: Nick Summerton
Institution: University of Hull
Language: English
Subfield: Family Practice
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2172577020