Evidence map›Paper›PMID 41803233›Full record

ArticleNPJ digital medicine2026

Immersive competence as a source of bias in virtual reality clinical assessment.

Jan Schaal, Tobias Leutritz, Marco Lindner, Alexander Zamzow, Joy Backhaus, Sarah König, Tobias Mühling

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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

7 authors.

Jan SchaalUniversity Hospital Würzburg, Institute of Medical Teaching and Medical Education Research, Würzburg, Germany.
Tobias LeutritzUniversity Hospital Würzburg, Institute of Medical Teaching and Medical Education Research, Würzburg, Germany.
Marco LindnerUniversity Hospital Würzburg, Institute of Medical Teaching and Medical Education Research, Würzburg, Germany.
Alexander ZamzowUniversity Hospital Würzburg, Institute of Medical Teaching and Medical Education Research, Würzburg, Germany.
Joy BackhausUniversity Hospital Würzburg, Institute of Medical Teaching and Medical Education Research, Würzburg, Germany.
Sarah KönigUniversity Hospital Würzburg, Institute of Medical Teaching and Medical Education Research, Würzburg, Germany.
Tobias MühlingUniversity Hospital Würzburg, Institute of Medical Teaching and Medical Education Research, Würzburg, Germany. muehling_t@ukw.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual reality (VR) is increasingly used for assessment in educational and clinical settings. However, users' immersive competence (IC)-the ability to navigate and operate VR systems-may introduce bias unrelated to clinical skills or patient functioning. In this randomized controlled trial, 88 medical students received either general IC training, general+specific IC training, or no structured training before completing a VR-based assessment scenario. Multimodal data were collected, including electrodermal activity, cognitive-load ratings, procedural efficiency, and usability barriers. Specific IC training improved performance compared with control (28.3% ± 10.3% vs. 21.2% ± 10.8%, p = 0.010, d = 0.67), moderated by procedural efficiency and increased cognitive load. Prior 3D experience did not predict performance in the control group, likely due to a floor effect, but did in the specific training group. These findings indicate that IC is a causal, modifiable factor in VR-based assessments and should be considered to ensure fair and valid evaluations.

Identifiers

PMID41803233
PMCPMC13049112

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.