Medical student syndrome: a bayesian reasoning failure

Medical Student Syndrome (MSS) is often understood as a manifestation of illness anxiety disorder where medical students misinterpret benign symptoms as signs of a serious illness. Current explanations rely heavily on psychological factors; however, these fail to capture the systemic reasoning errors that underly MSS. This paper proposes a mixed model: that MSS results from a combination of the Bayesian reasoning failure of base rate neglect and psychological stressors. An observational cross-sectional study of medical students was conducted $$\left(n=112\right)$$. Participants completed an anonymous survey assessing self-reported MSS, responses to medical diagnostic scenarios modelled on Kahneman and Tversky’s “librarian or farmer” problem, and prioritisation of diagnostic reasoning components. Descriptive statistics and categorical analysis of qualitative responses were performed. A history of MSS was reported by 56.3% of participants. Diagnostic accuracy was low for both medical scenarios (48.3% correct for differentiating between stroke and migraine; 33.3% for differentiating between pheochromocytoma and panic disorder) and the Bayesian reasoning task (41.1% correct). 71% of students who answered medical questions incorrectly also failed the Bayesian task, suggesting a shared underlying process. 14% of students with a history of MSS answered both questions correctly. Students ranked pathophysiology and symptoms of the presenting complaint as most important in diagnosis, and epidemiology as the least important (mean rank = 8.8/9). Self-analysis of errors showed that most students attributed their mistakes to poor probabilistic reasoning rather than a lack of knowledge. The findings support the explanation of MSS as partially a statistical reasoning error rather than a purely psychological condition. Training in Bayesian reasoning, integration of epidemiology into diagnostic teaching, and diversification of assessment formats may mitigate MSS and enhance diagnostic accuracy for medical students.