Evidence map›Paper›PMID 40340774›Full record

Trial reportBMC medical education2025

Enhancing laparoscopic surgery training: a comparative study of traditional models and automated error detection system.

Yitian Luo, Jingjie Wang, Zongting Yan, Jingjing He, Liye Fu, Shenghan Wang, Ying Han, Yaoyu Fu, Xiandi Wang, Kang Li and 2 more

Abstract readComparative StudyRandomized Controlled Trial
In one paragraph

Trial report 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Yitian LuoWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Jingjie WangPittsburgh Institute, Sichuan University, Chengdu, China.
Zongting YanPittsburgh Institute, Sichuan University, Chengdu, China.
Jingjing HePittsburgh Institute, Sichuan University, Chengdu, China.
Liye FuPittsburgh Institute, Sichuan University, Chengdu, China.
Shenghan WangPittsburgh Institute, Sichuan University, Chengdu, China.
Ying HanDepartment of Medical Simulation Center, West China Hospital, Sichuan University, Chengdu, China.
Yaoyu FuDepartment of Medical Simulation Center, West China Hospital, Sichuan University, Chengdu, China.
Xiandi WangDepartment of Medical Simulation Center, West China Hospital, Sichuan University, Chengdu, China.
Kang LiWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Rong Yin *West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China. rong.yin@scupi.cn.
Dan Pu *Department of Medical Simulation Center, West China Hospital, Sichuan University, Chengdu, China. 925102224@qq.com.

Funding

Science and Technology Department of Sichuan Province 24KJPX0119Sichuan University GSSCU2024028West China Hospital, Sichuan University ZYYC21004
6 · The paper itself

Abstract

backgroundAlthough beneficial for patients through its minimally invasive nature, laparoscopic surgery creates unique training challenges due to limited instrument maneuverability, absence of stereovision, and inadequate real-time feedback. Traditional training models rely on subjective instructor evaluations, which are time-consuming and lack objective error detection. This study evaluates the efficacy of an Automated Error Detection System (AEDS), designed to provide real-time feedback on mistouch error counts, in improving laparoscopic skill acquisition compared to conventional methods.

methodsForty novice participants were recruited and randomized into Group A (AEDS-enhanced training) and Group B (traditional training). Group A underwent a crossover design: 10 min of baseline training without AEDS followed by 10 min with AEDS. Group B completed 20 min of traditional training. The training program encompassed standardized laparoscopic tasks designed to simulate real surgical procedures. Performance metrics, including task completion time and the number of errors made, were recorded for each participant through AEDS. Confidence levels were assessed through self-reported questionnaires. Furthermore, statistical analysis was performed to evaluate the effectiveness of AEDS. A paired t-test was utilized to assess error reductions within the AEDS group, and Bland-Altman analysis was used to analyze the self-estimate error bias. Also, a Wilcoxon signed-rank test evaluated improvements in confidence levels attributable to the system, while a Mann-Whitney U test was conducted to compare performance metrics between the AEDS and traditional training groups.

resultsGroup A demonstrated a 24% reduction in errors post-AEDS (mean: 78.1 to 59.4, p < 0.001), outperforming Group B (mean: 67.4, p < 0.001). Participants significantly underestimated errors without AEDS (mean bias: +9.9 errors). Confidence levels in Group A increased from 2.4 to 3.6, significantly surpassing Group B's improvement (median: 3) (p < 0.001). Real-time feedback bridged perceptual gaps, enhancing both technical precision and self-assessment accuracy.

conclusionThe integration of AEDS into laparoscopic training significantly reduces operational errors, accelerates skill acquisition, and boosts trainee confidence by providing objective feedback. These findings advocate for adopting AEDS in surgical education to standardize training outcomes, mitigate overconfidence, and improve patient safety. Future studies should explore AEDS scalability across advanced procedural modules and diverse trainee cohorts. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Clinical CompetenceLaparoscopyMedical ErrorsSimulation TrainingAdultCross-Over StudiesFemaleHumansMaleLaparoscopic surgeryMedical trainingSimulationsSurgery error reduction

Identifiers

PMID40340774
PMCPMC12063224

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.