# Causal Mechanistic Reasoning as a Tool to Explore Medical Students’ Predictions of Pharmacology Phenomenon: Connecting Core Concepts with Clinical Applications

## TL;DR

This study explores how medical students use causal mechanistic reasoning to predict and explain adverse drug effects, finding that it significantly improves their understanding of pharmacology.

## Contribution

The study introduces the use of causal mechanistic reasoning in pharmacology education and demonstrates its effectiveness in predicting adverse drug effects.

## Key findings

- 67% of students correctly identified urogenital infections as an adverse effect of SGLT2 inhibitors.
- Only 25% provided a fully causal mechanistic explanation for the adverse effect.
- A strong association was found between using CMR and correctly predicting the adverse drug effect.

## Abstract

Prior research in education has identified that causal mechanistic reasoning (CMR) can enhance understanding of causal relationships and support the construction of explanations and predictions. However, the literature lacks information about how CMR is used among medical students or in pharmacology education. This study investigated how medical students utilize CMR to predict and explain adverse drug effects (ADE) as a pharmacological phenomenon.

Pre-clerkship osteopathic medical students enrolled at a large American university were asked to predict and explain their reasoning related to adverse effects caused by SGLT2 inhibitors. Their responses guided the development of a coding scheme to characterize the degree to which students used CMR. Pearson’s chi-squared tests were applied to analyze the presence and strength of the relationships between overall ADE predictions and the type of CMR used.

Sixty-seven percent of the students (N = 88) correctly identified urogenital infections as a possible ADE caused by SGLT2 inhibitors; however, only 25% provided a fully causal mechanistic account. However, we identified a significant association of large effect size between using CMR and correctly predicting the ADE (χ2 = 56.129, p-value < 0.001, Cramer’s V = 0.799).

CMR can be a useful tool for supporting medical students’ understanding of pharmacological phenomena and solidifying students’ learning toward an effective application in future clinical practice. This research highlights how more integrative, mechanism-focused curricula may be a promising area of future research in pharmacology education research.

How students employ mechanistic reasoning to connect foundational biomedical sciences (e.g., physiology, microbiology, biochemistry) with core pharmacological concepts, such as pharmacodynamics, to think through the potential adverse effects of a drug class (SGLT2 inhibitor). Causal mechanistic reasoning (CMR) can be used to understand how students use their knowledge of the underlying entities to explain a phenomenon and address pharmacology-specific questions.

The online version contains supplementary material available at 10.1007/s40670-025-02432-6.