Natural frequency tree- versus conditional probability formula-based training for medical students' estimation of...

Area of Science:

• Medical Education
• Biostatistics
• Clinical Decision Making

Background:

• Medical students and professionals often misinterpret medical test results, leading to suboptimal decisions.
• Natural frequency tree-based training (NF-TT) is a proposed method to improve understanding of predictive test values.
• This study compared NF-TT with conventional conditional probability formula-based training (CP-FT).

Purpose of the Study:

• To compare the effectiveness of NF-TT versus CP-FT in medical students' ability to estimate medical test predictive values.
• To identify student characteristics influencing the effectiveness of NF-TT.
• To assess the transfer of learned skills to non-medical contexts.

Main Methods:

• A randomized controlled trial was conducted with 231 medical students in South Korea.
• Participants were assigned to watch either a 15-minute NF-TT or CP-FT video.
• The primary outcome was accuracy in estimating predictive values, measured immediately post-intervention and at one-month follow-up.

Main Results:

• Overall, NF-TT showed no significant advantage over CP-FT in estimating predictive values immediately after training or at follow-up.
• NF-TT was significantly more effective than CP-FT for participants without prior relevant training at follow-up.
• NF-TT also demonstrated superior transfer of learning to non-medical contexts for this subgroup.

Conclusions:

• NF-TT may be most beneficial when introduced early in medical education, prior to extensive exposure to formula-based methods.
• Targeted implementation of NF-TT could enhance probabilistic reasoning skills in specific student populations.
• Further research may explore optimal curriculum integration for NF-TT.