Objective: The objective of the study was to assess the validity of the NASA-TLX score in rating the workload of pediatric robotic operations.
Methods: The workload of 230 pediatric gastrointestinal and thoracic robotic operations was rated using the NASA-TLX score. The difference between the high workload group and the low workload group in each subscale of the NASA-TLX score was analyzed. The correlation of each subscale with the total workload score in the high workload group and low workload group was also analyzed. A logistic regression analysis was subsequently conducted to assess the effects of different factors (sex, age, weight, procedure duration, procedure specialties, combined malformation and blood loss) on the workload.
Results: The average NASA-TLX score was 56.5 ± 5.1 for the total group, 56.9 ± 5.0 for the gastrointestinal group and 54.6 ± 4.8 for the thoracic group, p = 0.007. The score of the high workload group was 62.7 ± 3.2, while it was 50.6 ± 2.7 for the low workload group (p < 0.001). The score on each subscale was also significantly different between the high and low workload groups. In the high workload group, a stronger correlation was observed between the total score and TD and Fr and a lower correlation with MD and Pe. In the low workload group, all six subscales showed a moderate correlation with the total score. A multivariate logistic regression analysis revealed that the procedure duration was an independent influencing factor for a higher workload score.
Conclusions: NASA-TLX is a valid tool to rate the surgeon's workload in pediatric robotic surgery. A longer operative time contributes to a higher workload.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00464-023-09959-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Preventive Effects of Resistance Training on Hemodynamics and Kidney Mitochondrial Bioenergetic Function in Ovariectomized Rats.
Int J Mol Sci
December 2024
Department of Physiological Sciences, Interinstitutional Post-Graduate Program of Physiological Sciences, Federal University of São Carlos (UFSCar), São Carlos 13.566-490, SP, Brazil.
Menopause occurs due to the depletion of the ovarian reserve, leading to a progressive decline in estrogen (E2) levels. This decrease in E2 levels increases the risk of developing several diseases and can coexist with chronic kidney disease (CKD). Arterial hypertension (AH) is another condition associated with menopause and may either contribute to or result from CKD.
View Article and Find Full Text PDFSoldier performance management: insights from boots on ground research and recommendations for practitioners.
BMJ Mil Health
January 2025
Neuromuscular Research Laboratory/Warrior Human Performance Research Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Theoretically, the serial measurement of biomarkers to monitor physiological responses to military training could be used to mitigate musculoskeletal injury risk and better understand the recovery status of personnel. To date, the cost and scalability of these initiatives have impeded their uptake by defence organisations. However, advances in technology are increasing the accessibility of a range of health and performance biomarkers.
View Article and Find Full Text PDFDevelopment and validation of spatial disorientation scenarios using virtual reality and motion simulator.
Appl Ergon
January 2025
Samsung Electronics Co. Ltd., South Korea. Electronic address:
Spatial Disorientation (SD) can cause critical aviation accidents by adversely affecting the pilot's ability to perform a flight mission. One of the strategies to improve pilots' ability to deal with SD is to perform SD training using Virtual Reality and Motion Simulator (VRMS) system. However, there is still a lack of studies that investigated the application of VRMS for SD training.
View Article and Find Full Text PDFSemi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.
J Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.
Eur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!