ArticleJournal of graduate medical education2023
Comparing Machine Learning Models and Human Raters When Ranking Medical Student Performance Evaluations.
Article in Journal of graduate medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Machine learning-driven prediction of healthcare resource trends and optimal allocation strategies: a data-driven approach.BMC health services research · 2026Article
- The Medical Student Performance Evaluation in global medical education: addressing a missing Middle Eastern perspective.Frontiers in medicine · 2026Review
- The Use of Artificial Intelligence in Residency Application Evaluation-A Scoping Review.Journal of graduate medical education · 2025Article
- Trainee Identification of the Self-efficacy Domains Needed to Succeed in Undergraduate and Graduate Medical Education.Medical science educator · 2025Article
- Review
- Comparing paper-based and mobile application for rank-based peer assessment in interprofessional education: before, during, and after the COVID-19 pandemic.BMC medical education · 2024Article
- Measuring and Predicting Faculty Consensus Rankings of Standardized Letters of Evaluation.Journal of graduate medical education · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The Medical Student Performance Evaluation (MSPE), a narrative summary of each student's academic and professional performance in US medical school is long, making it challenging for residency programs evaluating large numbers of applicants. Objective: To create a rubric to assess MSPE narratives and to compare the ability of 3 commercially available machine learning models (MLMs) to rank MSPEs in order of positivity. Methods: Thirty out of a possible 120 MSPEs from the University of Central Florida class of 2020 were de-identified and subjected to manual scoring and ranking by a pair of faculty members using a new rubric based on the Accreditation Council for Graduate Medical Education competencies, and to global sentiment analysis by the MLMs. Correlation analysis was used to assess reliability and agreement between student rank orders produced by faculty and MLMs. Results: The intraclass correlation coefficient used to assess faculty interrater reliability was 0.864 ( Conclusions: The rubric for manual grading provided reliable overall scoring and ranking of MSPEs. The MLMs accurately detected positive sentiment in the MSPEs but were unable to provide reliable rank ordering.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.