Fig. 1

Example of a clinical case question associated with a radiological image. Graph A shows the students’ responses, while graph B represents the distribution of students according to their probability of answering the question correctly (y-axis) based on their ability level in the exam (x-axis). “Ability” refers to the theoretical estimation of the student’s knowledge in the exam. Graph C represents the point at which this question best discriminates among the knowledge levels of the entire sample (x-axis). Both graphs belong to the Item Response Theory (IRT) using the Two-Parameter Logistic (2-PL) probability model. In this case, the question demonstrates excellent quality (rpbis 0.4286) due to a well-crafted and comprehensive statement, concise and precise answers, a typical image relevant to the clinical scenario being queried, and clear instructions in the statement indicating where the student should focus to avoid vague interpretations of other findings. Furthermore, since the concept being assessed is specific, with an adequate scope in the field of medicine and sufficient scientific evidence beyond any subjective interpretation, an excellent quality is achieved. The 2PL probability model demonstrate how students in the strong group perform better than those in the weak group (B), saturating the curve at a knowledge level close to 30% of the overall distribution of knowledge in the exam (C)