Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The X̄ Chart00:58

The X̄ Chart

510
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
510
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

704
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
704
The R Chart01:02

The R Chart

447
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
447
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

519
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
519

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Methylene-Bridged Melt-Castable Explosives with Enhanced Thermal Stability and Detonation Performance.

Organic letters·2026
Same author

Clinical significance and mutation analysis of HBsAg and Anti-HBs coexistence in Chronic Hepatitis B.

Virus research·2026
Same author

Patent Landscape and Technological Trajectory of Drug Delivery Systems for Overcoming Drug Resistance in Non-small Cell Lung Cancer.

Recent patents on anti-cancer drug discovery·2026
Same author

Olanzapine-Associated Hepatotoxicity in Bipolar Disorder: A Multicenter Real-World Study of Prevalence, Risk Factors, and Outcomes.

Drug design, development and therapy·2026
Same author

Network-based analysis reveals potential microRNA regulation of oncogenic pathways in SOX10-depleted uveal melanoma.

Cellular and molecular life sciences : CMLS·2026
Same author

Bernoulli-guided multi-scale EKF method for motion error compensation in phase-shifting profilometry.

Optics letters·2026

Related Experiment Video

Updated: Mar 1, 2026

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.7K

A new VLAD-based control chart for detecting surgical outcomes.

Jin Yue1, Xin Lai2, Liu Liu1

  • 1School of Mathematics and VC and VR Lab, Sichuan Normal University, Chengdu, Sichuan, China.

Statistics in Medicine
|June 8, 2017
PubMed
Summary

This study introduces a new risk-adjusted exponentially weighted moving average VLAD (RAEV) chart to detect changes in surgical quality. The RAEV chart provides defined control limits for Variable Life-Adjusted Displays, improving surgical risk monitoring.

Keywords:
VLADexponentially weighted moving averagerisk-adjustedsurgical outcomes

More Related Videos

Simulator Training for Endovascular Neurosurgery
08:08

Simulator Training for Endovascular Neurosurgery

Published on: May 6, 2020

4.2K

Related Experiment Videos

Last Updated: Mar 1, 2026

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.7K
Simulator Training for Endovascular Neurosurgery
08:08

Simulator Training for Endovascular Neurosurgery

Published on: May 6, 2020

4.2K

Area of Science:

  • Medical Statistics
  • Surgical Quality Improvement
  • Health Services Research

Background:

  • Timely detection of surgical quality changes is crucial for patient safety.
  • Variable Life-Adjusted Display (VLAD) is used in healthcare but lacks defined control limits.
  • Existing VLAD charts alone cannot reliably indicate significant surgical quality shifts.

Purpose of the Study:

  • To propose a novel risk-adjusted exponentially weighted moving average VLAD (RAEV) chart.
  • To provide a defined control limit for VLAD charts.
  • To enhance the detection of shifts in surgical risk.

Main Methods:

  • Development of the RAEV chart, integrating risk adjustment and exponentially weighted moving averages with VLAD.
  • Design of a control limit specifically for the RAEV-VLAD framework.
  • Simulation studies to assess the RAEV chart's efficiency in detecting various shift magnitudes.

Main Results:

  • The proposed RAEV chart demonstrates efficient detection of surgical risk shifts.
  • The RAEV chart provides a viable control limit for VLAD implementation.
  • Simulations confirm the chart's effectiveness across different shift sizes.

Conclusions:

  • The RAEV chart offers a statistically robust method for monitoring surgical quality.
  • Defined control limits enhance the interpretability and utility of VLAD in surgical practice.
  • This approach improves the ability to identify and respond to changes in surgical outcomes.