基于自然频率树与条件概率公式的医学生预测值估算培训：一项随机对照试验。

Natural frequency tree- versus conditional probability formula-based training for medical students' estimation of screening test predictive values: a randomized controlled trial.

机构信息

Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, South Korea.

Department of Social and Preventive Medicine, Hallym University College of Medicine, Chuncheon, South Korea.

出版信息

BMC Med Educ. 2024 Oct 24;24(1):1207. doi: 10.1186/s12909-024-06209-0.

DOI:10.1186/s12909-024-06209-0

PMID:39449124

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11515371/

Abstract

BACKGROUND

Medical students and professionals often struggle to understand medical test results, which can lead to poor medical decisions. Natural frequency tree-based training (NF-TT) has been suggested to help people correctly estimate the predictive value of medical tests. We aimed to compare the effectiveness of NF-TT with conventional conditional probability formula-based training (CP-FT) and investigate student variables that may influence NF-TT's effectiveness.

METHODS

We conducted a parallel group randomized controlled trial of NF-TT vs. CP-FT in two medical schools in South Korea (a 1:1 allocation ratio). Participants were randomly assigned to watch either NF-TT or CP-FT video at individual computer stations. NF-TT video showed how to translate relevant probabilistic information into natural frequencies using a tree structure to estimate the predictive values of screening tests. CP-FT video showed how to plug the same information into a mathematical formula to calculate predictive values. Both videos were 15 min long. The primary outcome was the accuracy in estimating the predictive value of screening tests assessed using multiple-choice questions at baseline, post-intervention (i.e., immediately after training), and one-month follow-up. The secondary outcome was the accuracy of conditional probabilistic reasoning in non-medical contexts, also assessed using multiple-choice questions, but only at follow-up as a measure of transfer of learning. 231 medical students completed their participation.

RESULTS

Overall, NF-TT was not more effective than CP-FT in improving the predictive value estimation accuracy at post-intervention (NF-TT: 87.13%, CP-FT: 86.03%, p = .86) and follow-up (NF-TT: 72.39%, CP-FT: 68.10%, p = .40) and facilitating transfer of training (NF-TT: 75.54%, CP-FT: 71.43%, p = .41). However, for participants without relevant prior training, NF-TT was more effective than CP-FT in improving estimation accuracy at follow-up (NF-TT: 74.86%, CP-FT: 58.71%, p = .02) and facilitating transfer of learning (NF-TT: 82.86%, CP-FT: 66.13%, p = .04).

CONCLUSIONS

Introducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit.

TRIAL REGISTRATION

Korea Disease Control and Prevention Agency Clinical Research Information Service KCT0004246 (the date of first trial registration: 27/08/2019). The full trial protocol can be accessed at https://cris.nih.go.kr/cris/search/detailSearch.do?seq=15616&search_page=L .

摘要

背景

医学生和专业人员经常难以理解医学检验结果，这可能导致医疗决策不当。自然频率树基训练 (NF-TT) 已被提议用于帮助人们正确估计医学检验的预测值。我们旨在比较 NF-TT 与传统条件概率公式为基础的训练 (CP-FT) 的有效性，并研究可能影响 NF-TT 有效性的学生变量。

方法

我们在韩国的两所医学院进行了 NF-TT 与 CP-FT 的平行组随机对照试验（分配比例为 1:1）。参与者被随机分配在个人电脑工作站上观看 NF-TT 或 CP-FT 视频。NF-TT 视频展示了如何使用树结构将相关概率信息转换为自然频率，以估计筛选测试的预测值。CP-FT 视频展示了如何将相同的信息插入数学公式以计算预测值。两个视频均为 15 分钟长。主要结局是使用多项选择题在基线、干预后（即培训后立即）和一个月随访时评估筛选测试预测值的准确性。次要结局是在非医学背景下进行条件概率推理的准确性，也使用多项选择题评估，但仅在随访时作为学习转移的衡量标准。共有 231 名医学生完成了他们的参与。

结果

总体而言，NF-TT 在干预后（NF-TT：87.13%，CP-FT：86.03%，p=0.86）和随访时（NF-TT：72.39%，CP-FT：68.10%，p=0.40）提高预测值估计准确性方面并不优于 CP-FT，也未促进培训转移（NF-TT：75.54%，CP-FT：71.43%，p=0.41）。然而，对于没有相关先验培训的参与者，NF-TT 在随访时提高估计准确性（NF-TT：74.86%，CP-FT：58.71%，p=0.02）和促进学习转移（NF-TT：82.86%，CP-FT：66.13%，p=0.04）方面优于 CP-FT。