Evidence map›Paper›PMID 31109315›Full record

ArticleBMC medical education2019

Multivariable analysis of factors associated with USMLE scores across U.S. medical schools.

Arash Ghaffari-Rafi, Rachel Elizabeth Lee, Rui Fang, J Douglas Miles

Open access · goldAbstract read
In one paragraph

Article in BMC medical education, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.7field-weighted citation impact, top 11% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 31 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 2 countries.

Arash Ghaffari-RafiUniversity of Hawai'i at Mānoa John A. Burns School of Medicine, Honolulu, Hawai'i, USA. arash.ghaffari-rafi.18@ucl.ac.uk.ORCID http://orcid.org/0000-0002-6098-8036
Rachel Elizabeth LeeUniversity of Hawai'i at Mānoa John A. Burns School of Medicine, Honolulu, Hawai'i, USA.
Rui FangUniversity of Hawai'i at Mānoa John A. Burns School of Medicine, Honolulu, Hawai'i, USA.
J Douglas MilesUniversity of Hawai'i at Mānoa John A. Burns School of Medicine, Honolulu, Hawai'i, USA.
University of Hawaiʻi at Mānoa · US

Funding

RESEARCH DESIGN AND BIOSTATISTICS COREU54MD007584 · NIMHD · UNIVERSITY OF HAWAII AT MANOA · PI MOKUAU, NOREEN, SHIRAMIZU, BRUCE T · 2012 to 2018
$21.1M
NIMHD NIH HHS U54 MD007584
6 · The paper itself

Abstract

backgroundGauging medical education quality has always remained challenging. Many studies have examined predictors of standardized exam performance; however, data sets do not distinguish by institution or curriculum. Our objective is to present a summary of variables associated with the United States Medical Licensing Examination (USMLE) scores, and thus identify institutions (and therefore curriculums) which deviate from trend lines by producing higher USMLE scores despite having lower entrance grade point averages and medical college admissions test (MCAT) scores.

methodsData was obtained from U.S. News and World Report's 2014 evaluation of allopathic U.S. medical schools. A univariate analysis was performed first for each variable using two sample t-test or Wilcoxon rank sum test for categorical variables, and Pearson or Spearman correlation coefficients for continuous variables. A multivariable linear regression model was developed to identify the factors contributing to USMLE scores. All statistical analyses were two-sided and performed using SAS software version 9.4 (SAS Institute Inc., Cary, NC).

resultsUnivariate analysis reveals a significant association between USMLE Step 1 and 2 scores with medical college admissions test scores, grade point averages, school type (private vs. public), full-time faculty-to-student ratio, National Institute of Health funds, residency director assessment score, peer assessment score, and class size. Of these nine variables, MCAT scores and Step 1 scores display the strongest correlation (corr = 0.72, P < .0001). Multivariable analysis also supports a significant association between MCAT scores and Step scores, meanwhile National Institute of Health funding size demonstrates a negative correlation with USMLE Step 2 scores. Although MCAT scores and National Institute of Health funds are significantly associated with USMLE performance, six outlier institutions were identified, producing higher USMLE scores than trend line predictions.

conclusionsOutlier institutions produce USMLE scores that do not follow expected trend lines. Their performance might be explainable by differences in curriculum. Having identified these institutions, their curriculums can be further studied to determine what factors enhance student learning.

Indexed as

CurriculumSchools, MedicalCertificationCollege Admission TestDatabases, FactualEducational MeasurementEducation, Medical, UndergraduateHumansLicensure, MedicalProgram EvaluationStudents, MedicalUnited StatesAssessmentCurriculumEvaluationStudent learningUnited States medical licensing examinationUSMLE

Identifiers

PMID31109315
PMCPMC6528346
OpenAlexW2947963864

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.