ArticleBMC medical education2024
When can we identify the students at risk of failure in the national medical licensure examination in Japan using the predictive pass rate?
Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Impact of longitudinal integrated clerkships on national medical licensing examination (Japan) performance.Medical education online · 2026Article
- Latent class analysis of medical students by admission type in Korea: effects on academic performance and career paths.Korean journal of medical education · 2026Article
- What Are the Opportunities and Challenges of Using AI in Medical Education in Vietnam?JMIR medical education · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
contextFailure of students to pass the National Medical Licensure Examination (NMLE) is a major problem for universities and the health system in Japan. To assist students at risk for NMLE failure as early as possible after admission, this study investigated the time points (from the time of admission to graduation) at which predictive pass rate (PPR) can be used to identify students at risk of failing the NMLE.
methodsSeven consecutive cohorts of medical students between 2012 and 2018 (n = 637) at the Gifu University Graduate School of Medicine were investigated. Using 7 variables before admission to medical school and 10 variables after admission, a prediction model to obtain the PPR for the NMLE was developed using logistic regression analysis at five time points, i.e., at admission and the end of the 1st, 2nd, 4th, and 6th grades. All students were divided into high (PPR < 95%) and low (PPR ≥ 95%) risk groups for failing the NMLE at the five time points, respectively, and the movement between the groups during 6 years in school was simulated.
resultsMedical students who passed the NMLE had statistically significant factors at each of the 5 time points, and the number of significant variables increased as their grade in school advanced. In addition, two factors extracted at admission were also selected as significant variables at all other time points. Especially, age at entry had a consistent and significant effect during medical school.
conclusionsRisk analysis based on multiple variables, such as PPR, can inform more effective intervention compared to a single variable, such as performance in the mock exam. A longer prospective study is required to confirm the validity of PPR.
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