Evidence map›Paper›PMID 41250140›Full record

ArticleBMC medical education2025

A pilot study of digital technology combined with case-based learning in clinical implantology training for specialists.

Cai Zhuoying, Liu Yanming

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Trial
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

2 authors.

Cai ZhuoyingThe Second Affiliated Hospital of Zhejiang University, Hangzhou, 310000, Zhejiang, China. caizhuoying@zju.edu.cn.
Liu YanmingThe Second Affiliated Hospital of Zhejiang University, Hangzhou, 310000, Zhejiang, China.

Funding

Teaching Reform Project of the Second Clinical Medical College, Zhejiang University School of Medicine 20210216
6 · The paper itself

Abstract

objectiveTo explore the feasibility and potential benefits of combining digital technology with case-based learning (CBL) in teaching clinical knowledge and skills in implantology for oral and maxillofacial surgery specialists.

methodsSix attending doctors without previous implant training were enrolled as the experimental group and received a training program that combined digital navigation technology with CBL. Post-course theoretical and procedural scores were assessed, and trainees' satisfaction was recorded. Outcomes were descriptively compared with scores of doctors who had previously completed traditional specialist training, and the preliminary nature of this comparison was acknowledged.

resultsThe experimental group demonstrated significant improvement in both theoretical and procedural skills from pre- to post-course assessments, accompanied by high satisfaction with the training. Although they started from a lower baseline, their post-course performance was similar to that of the clinicians who had completed traditional specialist training.

conclusionThe integration of digital navigation into a CBL framework was acceptable to trainees and showed potential educational value in early implant training. It helped strengthen procedural understanding and case analysis. As this was a pilot study with limited numbers, further research is planned to confirm its effectiveness and scope of application.

Indexed as

Dental ImplantationDigital TechnologyOral and Maxillofacial SurgeonsProblem-Based LearningSurgery, OralAdultClinical CompetenceFeasibility StudiesFemaleHumansMalePilot ProjectsCase-Based LearningDigital technologyImplantologyOral and Maxillofacial SurgerySpecialist training

Identifiers

PMID41250140
PMCPMC12625070

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.