Clinical meaning
Clinical reasoning integrates pattern recognition (System 1, fast/intuitive) with analytical reasoning (System 2, slow/deliberate). Bayesian reasoning adjusts disease probability based on sequential evidence: pre-test probability (based on prevalence, risk factors, presentation) is modified by test characteristics (sensitivity, specificity, likelihood ratios) to yield post-test probability. A test's utility depends on pre-test probability: high-sensitivity tests are best for ruling OUT disease (SnNout: Sensitive test, Negative result, rules Out); high-specificity tests are best for ruling IN disease (SpPin: Specific test, Positive result, rules In). The NP applies illness scripts (structured mental models of diseases with predisposing factors, pathophysiology, and clinical features), generates differential diagnoses, and systematically narrows them using history, exam, and testing.
Diagnosis & workup
Diagnostics & workup: - Sensitivity: true positive rate; the ability of a test to correctly identify disease; high sensitivity → low false negative rate → good for screening/ruling out (SnNout) - Specificity: true positive rate for non-disease; ability to correctly identify absence of disease; high specificity → low false positive rate → good for confirming/ruling in (SpPin) - Likelihood ratios: LR+ = sensitivity/(1-specificity); LR- = (1-sensitivity)/specificity; LR+ >10 or LR- <0.1 are strong; modify pre-test probability - Positive predictive value: probability of disease given positive test; affected by prevalence - Negative predictive value: probability of no disease given negative test; high-sensitivity tests in low-prevalence settings have high NPV - Clinical prediction rules: validated scoring systems that standardize probability assessment (HEART, Wells, PERC, Ottawa rules, Centor/McIsaac, CURB-65)