Evaluating the Reliability of Remote Video Based Assessment Metrics for the Advanced Training in Laparoscopic Suturing (ATLAS) Curriculum–a Pilot Study
Global Surgical Education - Journal of the Association for Surgical Education(2024)
Lahey Hospital and Medical Center
Abstract
The Advanced Training in Laparoscopic Suturing (ATLAS) curriculum was created to address gaps in advanced laparoscopic suturing training and has been previously studied using in-person assessment (IPA). This study aimed to evaluate the reliability of a remote, asynchronous video-based assessment (VBA) protocol by comparing to IPA to build validity data for the ATLAS curriculum. Three surgeons (two trainees, one expert) at a single institution performed five repetitions of each ATLAS task. Each performance underwent IPA by a single rater and remote VBA by two independent raters. Videos were de-identified and randomized prior to scoring. Task scores were calculated using time and error measurements per the ATLAS scoring rubric. The VBA scores were averaged and compared to IPA scores by calculating intraclass correlation coefficients (ICC). A total of 90 task performances were reviewed. A higher number of errors were detected in VBA than in IPA (393.5 vs. 323) with higher detection frequency in nine of fifteen errors. Average VBA scores were lower than average IPA scores across all tasks, but with moderate-to-excellent reliability for each task; the ICC of all tasks combined was 0.93 (P < 0.001, 95
MoreTranslated text
Key words
Laparoscopic suturing,Video-based assessment,Surgical education,Surgical curriculum
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2013
被引用26 | 浏览
2015
被引用39 | 浏览
A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.
2016
被引用25789 | 浏览
2019
被引用7 | 浏览
2019
被引用10 | 浏览
2021
被引用12 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined