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

Random Error01:04

Random Error

10.2K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
10.2K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

12.2K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
12.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

8.5K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
8.5K
Contaminants and Errors01:16

Contaminants and Errors

623
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
623
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.5K
Errors and Mistakes in Surveying01:19

Errors and Mistakes in Surveying

1.0K
Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Consensus recommendations on lipohypertrophy: insights from an international panel of experts.

Diabetes research and clinical practice·2026
Same author

What do LLMs value? An evaluation framework for revealing subjective trade-offs in assessment of glycemic control.

Proceedings of machine learning research·2026
Same author

Real-World Evidence Assessment of the Risk of Nonfatal Stroke in Patients Prescribed SGLT2 Inhibitors.

Stroke research and treatment·2026
Same author

Corrigendum to "Clinical assessment and acceptance criteria for continuous glucose monitoring (CGM) system performance: A proposed guideline by the IFCC working group on CGM" [Clin. Chim. Acta 580 (2026) 120728].

Clinica chimica acta; international journal of clinical chemistry·2026
Same author

Continuous Glucose Monitoring and Mortality Risk Among U.S. Veterans Receiving Dialysis With Diabetes.

Diabetes care·2026
Same author

Glucose Dysregulation in Hantavirus Infection: A Signal Worth Watching With Continuous Glucose Monitoring.

Journal of diabetes science and technology·2026
Same journal

A Pilot Study on Disposal Practices and Environmental Awareness of Insulin-Related Devices Among People With Diabetes.

Journal of diabetes science and technology·2026
Same journal

Quality of In-Use Insulin Under Real-World Storage Conditions in Mwanza, Tanzania.

Journal of diabetes science and technology·2026
Same journal

Continuous Glucose Monitoring Metrics for Predicting Adverse Neonatal Outcomes in Individuals Undergoing Gestational Diabetes Screening.

Journal of diabetes science and technology·2026
Same journal

Diabetes Technologist: Optimal Use of Technology in Everyday Practice.

Journal of diabetes science and technology·2026
Same journal

AI-Driven Diabetes Care and Its Relevance in the Philippine Context: Opportunities and Persistent Digital Barriers.

Journal of diabetes science and technology·2026
Same journal

Ease of Use, Ease of Learning, and Convenience of the CagriSema Dual-Chamber Pen: Results From a Usability Study in Adults With Overweight, Obesity, or Type 2 Diabetes.

Journal of diabetes science and technology·2026
See all related articles

Related Experiment Video

Updated: Apr 18, 2026

Cryo-Electron Microscopic Grid Preparation for Time-Resolved Studies using a Novel Robotic System, Spotiton
08:59

Cryo-Electron Microscopic Grid Preparation for Time-Resolved Studies using a Novel Robotic System, Spotiton

Published on: February 25, 2021

4.4K

Computing the surveillance error grid analysis: procedure and examples.

Boris P Kovatchev1, Christian A Wakeman2, Marc D Breton2

  • 1University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA boris@virginia.edu.

Journal of Diabetes Science and Technology
|January 7, 2015
PubMed
Summary
This summary is machine-generated.

A new Surveillance Error Grid (SEG) software automates blood glucose monitoring (BGM) error analysis, classifying BGM data into 8 risk zones for improved visualization and clinical risk assessment.

Keywords:
blood glucose monitoringerror grid analysishyperglycemiahypoglycemiameter errors

More Related Videos

Single Particle Cryo-Electron Microscopy: From Sample to Structure
11:52

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

10.0K
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.7K

Related Experiment Videos

Last Updated: Apr 18, 2026

Cryo-Electron Microscopic Grid Preparation for Time-Resolved Studies using a Novel Robotic System, Spotiton
08:59

Cryo-Electron Microscopic Grid Preparation for Time-Resolved Studies using a Novel Robotic System, Spotiton

Published on: February 25, 2021

4.4K
Single Particle Cryo-Electron Microscopy: From Sample to Structure
11:52

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Published on: May 29, 2021

10.0K
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

1.7K

Area of Science:

  • Medical Informatics
  • Clinical Chemistry
  • Diabetes Technology

Background:

  • Blood glucose monitoring (BGM) errors pose clinical risks.
  • Existing Surveillance Error Grid (SEG) analysis is complex and requires automation.
  • Standardized risk assessment for BGM accuracy is crucial.

Purpose of the Study:

  • To introduce automated SEG software for BGM error analysis.
  • To classify BGM data into 8 distinct risk zones.
  • To enhance visualization of BGM data accuracy and associated clinical risks.

Main Methods:

  • Developed software to automate SEG analysis.
  • Processed 337,561 risk ratings from reference and BGM readings.
  • Utilized clinician-assessed treatment scenarios to define risk levels.
  • Linked SEG analysis to ISO 15197:2013 standards using simulated data.
  • Applied the software to previously published BGM performance data.

Main Results:

  • The SEG software successfully categorizes BGM data into 8 risk zones (none to extreme).
  • It computes data distribution within zones and identifies errors leading to hypo- or hyperglycemia.
  • Demonstrated software utility with simulated and real-world BGM data under various conditions.

Conclusions:

  • The SEG software effectively automates complex SEG analysis.
  • It provides a valuable tool for assessing clinical risk from BGM inaccuracies.
  • The software is applicable in critical settings like intensive care and disaster management.