Evidence map›Paper›PMID 42401913›Full record

ArticleBMC medical education2026

Extended reality and web-based imaging in case-based anatomy education: a two-year study of knowledge retention on anomalies and variations.

Güneş Aytaç, Kalāwena Kalehuawehe, Shawnea Pagat, Cemil Oktay, Christoph Rettenmeier, U-Young Lee, Rebecca Romine, Scott Lozanoff

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Güneş AytaçDepartment of Anatomy, Biochemistry & Physiology, John A. Burns School of Medicine, University of Hawai'i at Manoa, 651 Ilalo St, Honolulu, HI, 96813, USA. gunes@hawaii.edu.
Kalāwena KalehuaweheDivision of Mathematics, Natural and Health Sciences, University of Hawaii - West O'ahu, Kapolei, HI, USA.
Shawnea PagatDivision of Mathematics, Natural and Health Sciences, University of Hawaii - West O'ahu, Kapolei, HI, USA.
Cemil OktayDepartment of Radiology, Akdeniz University, Antalya, Turkey.
Christoph RettenmeierDepartment of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA.
U-Young LeeCatholic Institute for Applied Anatomy, Department of Anatomy, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Rebecca RomineDivision of Mathematics, Natural and Health Sciences, University of Hawaii - West O'ahu, Kapolei, HI, USA.
Scott LozanoffDepartment of Anatomy, Biochemistry & Physiology, John A. Burns School of Medicine, University of Hawai'i at Manoa, 651 Ilalo St, Honolulu, HI, 96813, USA.

Funding

UH Hilo COP A&RP20GM103466 · NIGMS · UNIVERSITY OF HAWAII AT MANOA · PI Peter R Hoffmann · 2012 to 2026
$60.3M
INBRE P20GM103466National Science Foundation 1855379NIGMS NIH HHS P20 GM103466
6 · The paper itself

Abstract

backgroundThis prospective repeated-measures quasi-experimental educational study, conducted across two consecutive first-year medical school cohorts, aimed to develop and implement case-based learning (CBL) modules using three-dimensional (3D) models and magnetic resonance imaging (MRI) within an extended reality (XR) environment. The intervention integrated anatomical variations and anomalies to enhance immediate learning and long-term knowledge retention.

methodsMRI data from selected donors at the John A. Burns School of Medicine (JABSOM) were used to create XR-CBL modules. First-year medical students from the classes of 2024 (Cohort 2021) (n = 61) and 2025 (Cohort 2022) (n = 67) participated. Before the session, students received an email detailing the activity. During the gross anatomy laboratory, they accessed a website to explore cases featuring learning objectives, clinical and radiological data, 3D models, and MRIs. The XR experience utilized desktop-based XR technology. An assessment was administered before, immediately after, and eight weeks post-intervention. Students also completed a mixed-format feedback survey.

resultsA repeated-measures ANOVA revealed a significant effect of the intervention on student performance across both cohorts. For Cohort 2021, Wilks' Lambda = 0.384, F (2, 59) = 47.27, p < 0.001, partial η² = 0.616. For Cohort 2022, Wilks' Lambda = 0.30, F (2, 65) = 72.72, p < 0.001, partial η² = 0.691. Follow-up pairwise comparisons with Bonferroni adjustments indicated significant improvements from pre-test to post-test and from pre-test to post-post-test for both cohorts (p < 0.01). There was no significant difference between the post-test and post-post-test scores (p = 1.00). In Cohort 2021, mean scores significantly increased from pre-test (3.9 ± 1.9) to post-test (6.2 ± 2.6) and were maintained at post-post-test (6.3 ± 1.7). Similarly, Cohort 2022 demonstrated significant gains from pre-test (4.3 ± 1.6) to post-test (7.6 ± 2.2), with high retention at post-post-test (6.3 ± 2.4). Pairwise comparisons confirmed that improvements from baseline were significant (p < 0.01), while no significant decay occurred between post- and post-post-assessments (p = 1.00).

conclusionsIncorporating multimodal resources, including anatomical variations and anomalies through CBL, into the gross anatomy laboratory was associated with significant score improvements and positive student perceptions. Learners reported that the interactive, imaging-based tools helped them better understand complex anatomical relationships, particularly when anatomical variability was highlighted.

Indexed as

AnatomyComputer-Assisted InstructionEducation, Medical, UndergraduateInternetMagnetic Resonance ImagingProblem-Based LearningRetention, PsychologyEducational MeasurementFemaleHumansImaging, Three-DimensionalMaleProspective StudiesStudents, MedicalAnatomical variationsAnatomy educationCase-based learningEducational measurementExtended realityVirtual reality

Identifiers

PMID42401913
PMCPMC13613780

What OpenQuestion holds

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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.