Evidence map›Paper›PMID 41039531›Full record

ArticleBMC medical education2025

Evaluation of a progressive laparoscopic training program for clinical professional master's degree residents in china: skill improvement and educational implications.

Xiang Xu, Feihong Xu, Xiaobao Cheng, Genggeng Wei, Kenan Xuan, Zhenquan Lu, Guixi Zhang, Shihong Luo, Juling Lu

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 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

9 authors.

Xiang Xu *Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518053, China.
Feihong Xu *Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518053, China.
Xiaobao Cheng *Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518053, China.
Genggeng WeiDepartment of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518053, China.
Kenan XuanShenzhen University Medical School, Shenzhen, Guangdong, 518053, China.
Zhenquan LuDepartment of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518053, China. luzq@hku-szh.org.
Guixi ZhangDepartment of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518053, China. zhanggx@hku-szh.org.
Shihong LuoDepartment of Education and Training, The University of Hong Kong- Shenzhen Hospital, Shenzhen, Guangdong, 518053, China.
Juling LuClinical Skills Simulation Training Centre of The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, 518053, China.

Funding

Medical Education Collaborative High-Quality Innovation and Development Research Project (YJXT20250508) YJXT20250508Shenzhen Health Commission 2020 High-Level Medical Team Introduction Project (SZSM202011001). SZSM202011001
6 · The paper itself

Abstract

backgroundWith the ongoing reform of clinical medical education in China, the training model for Clinical Professional Master's Degree Residents (CPMDRs) has become a crucial component of medical education. as a core technique in modern surgical practice, plays a pivotal role in standardized residency training. However, CPMDRs often face challenges in efficiently mastering laparoscopic skills within a limited training period. This study aims to evaluate the effectiveness of a progressive laparoscopic training program in enhancing the skills of CPMDRs and to explore its implications for clinical medical education in China.

methodsA three-month progressive laparoscopic training program was designed, incorporating multiple teaching methods such as knowledge acquisition, box trainer training, virtual reality (VR) simulation, ex vivo organ training, and animal-based surgical training. A total of 25 CPMDRs participated in the program. Their laparoscopic skills were evaluated before and after the training using task completion times and standardized performance metrics. In addition, a questionnaire was administered to assess participants' satisfaction with the program and their perceived improvement in competency.

resultsFollowing the training, participants demonstrated significant reductions in task completion times across all laparoscopic tasks (P < 0.001), particularly in visual targeting, precision transfer drills, and interrupted suturing. The program received high satisfaction ratings, with 80% of participants strongly agreeing that it stimulated their interest in learning. Furthermore, participants reported marked improvements in basic laparoscopic techniques, VR-based surgical skills, and animal-based procedural abilities.

conclusionThe progressive laparoscopic training program effectively enhanced the laparoscopic skills of CPMDRs and received high approval from participants. By employing a multidimensional teaching approach, this training model is adaptable to the resource conditions of different regions in China, demonstrating broad potential for widespread adoption. This study provides scientific evidence and practical support for optimizing the laparoscopic skills training system for CPMDRs in China.

Indexed as

Clinical CompetenceEducation, Medical, GraduateInternship and ResidencyLaparoscopyAdultChinaFemaleHumansMaleProgram EvaluationAnimal-Based surgery trainingChina medical educationClinical professional master's degree residentsLaparoscopic trainingProgressive training modelSkill improvementVirtual reality simulation

Identifiers

PMID41039531
PMCPMC12492852

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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.