Evidence map›Paper›PMID 42110437›Full record

ArticleFrontiers in medicine2026

Quantitative evaluation of 3D-printed physiological visualization tools in enhancing interns' knowledge retention and application.

Qiongting Luo, Wenwen Hou, Xiaofen Yu, Xinyu Wang, Zheng Wang

Abstract read
In one paragraph

Article in Frontiers in 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.

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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

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

5 authors.

Qiongting LuoHangzhou Medical College (Affiliated People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital), Hangzhou, China.
Wenwen HouHangzhou Medical College (Affiliated People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital), Hangzhou, China.
Xiaofen YuHangzhou Medical College (Affiliated People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital), Hangzhou, China.
Xinyu WangHangzhou Medical College (Affiliated People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital), Hangzhou, China.
Zheng WangHangzhou Medical College (Affiliated People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital), Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Traditional physiology teaching relies on 2D materials and static specimens, making it difficult to intuitively present complex anatomical structures and physiological mechanisms. 3D Intelligent Printing Technology (3DIPT) has demonstrated application value in surgical training, but its use in physiology education remains underexplored. Methods: A randomized controlled trial (RCT) was conducted, enrolling 120 undergraduate nursing interns who were randomly divided into a control group (traditional teaching) and an observation group (3DIPT-assisted teaching) with a 6-month intervention period. The observation group used 3D-printed models of key nursing-relevant organs; this paper partially presents those of the ovary, uterus, stomach, prostate, and kidney for clinical education and connected learning. Outcome measures included scores on physiology-related knowledge (nurse licensing examination simulation), Social Medical Curiosity (SMC), self-directed learning ability, mobile learning willingness, and Medical Students' Transformative Learning Readiness (MSTLR). Results: After the intervention, the observation group showed significantly higher scores than the control group in physiology knowledge (77.30 ± 9.65 vs. 67.36 ± 9.55, Conclusion: 3DIPT-assisted teaching can effectively improve nursing interns' mastery of physiology knowledge and core competencies such as medical interest and autonomous learning. It provides an intuitive visualization tool for physiology education and holds significant potential for advancing basic medical teaching reform.

Indexed as

3D intelligent printing technologyanatomical visualizationknowledge retentionmedical curiositynursing internsphysiology teachingself-efficacytransformative learning readiness

Identifiers

PMID42110437
PMCPMC13152813

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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.