Evidence map›Paper›PMID 42237286›Full record

Trial reportBMC medical education2026

A structured blended learning approach for medical imaging technology interns: a prospective randomized comparative study.

Hao Zheng, Huiping Jiang, Haibo Xia, Lida Mo, Junna Wang, Shiwei Wang

Abstract readRandomized Controlled TrialComparative Study
In one paragraph

Trial report 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.

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

6 authors.

Hao Zheng *The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310000, China.
Huiping Jiang *The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310000, China.
Haibo XiaThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310000, China.
Lida MoThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310000, China.
Junna WangThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310000, China.
Shiwei WangThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310000, China. wsw1204@zcmu.edu.cn.

Funding

Ministry of Education-Industry Collaboration and Cooperative Education Project 230901214131224Undergraduate Education and Teaching Reform Project of Zhejiang Chinese Medical University ZB24002
6 · The paper itself

Abstract

backgroundTo address radiation safety constraints and equipment shortages in Computed Tomography (CT) training, this study introduces a Structured Blended Learning (SBL) framework integrating Small Private Online Courses (SPOCs), Flipped Classroom (FC), Virtual Simulation (VS), and remote supervision enabled by 5G communication infrastructure. This approach bridges the theory-practice gap by providing a structured progression from knowledge acquisition to simulated and clinical practice.

methodsIn a prospective randomized comparative study, 60 medical imaging interns undergoing CT clinical rotations were randomly assigned to either the SBL group (n = 30) or the traditional lecture-based learning (LBL) group (n = 30). Training effectiveness was evaluated using continuous theoretical examination scores (t-tests), ordinal Mini-Clinical Evaluation Exercise (Mini-CEX) practical assessments (Mann-Whitney U tests), and frequency distribution data from Likert-scale surveys (χ² tests) assessing student satisfaction and perceived learning outcomes.

resultsThe SBL group consistently outperformed the LBL group across all theoretical assessments, yielding significantly higher mean scores in the pre-class, post-class, and final examinations (t = 2.391-2.779; all P < 0.05; Cohen's d = 0.586-0.717). Mini-CEX assessments revealed superior performance by the SBL group across six domains, including patient positioning and handling, radiation protection and safety, equipment operation and image acquisition, image quality evaluation, organization and efficiency, and overall professional competence (Z = 4.855-5.110; all P < 0.001; r = 0.627-0.660). Furthermore, the SBL group reported significantly higher satisfaction with teaching methods, professional skills development, and overall evaluation (χ² =12.76-37.49; all P < 0.01; Cramér's V=0.231-0.395) and perceived greater effectiveness in professional theoretical knowledge, practical and operational skills, self-directed learning, and overall teaching effectiveness (χ² = 18.27-27.04; all P < 0.01; Cramér's V = 0.276-0.336).

conclusionsThe SBL approach provides an effective, safe, and engaging pedagogical framework for training medical imaging interns in complex CT workflows. By leveraging digital technologies to address physical access and radiation safety constraints, this approach demonstrates potential for broader application in medical imaging technology education, although further multicenter studies are needed to confirm its generalizability.

Indexed as

Internship and ResidencyTomography, X-Ray ComputedClinical CompetenceComputer-Assisted InstructionEducational MeasurementFemaleHumansMaleProspective StudiesYoung AdultFlipped ClassroomMedical Imaging TechnologyRemote SupervisionSmall Private Online CoursesStructured Blended LearningVirtual Simulation

Identifiers

PMID42237286
PMCPMC13449503

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.