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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.7K
Randomized Experiments01:13

Randomized Experiments

7.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.3K
Random Error01:04

Random Error

1.6K
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...
1.6K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

81.9K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
81.9K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.2K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.2K

You might also read

Related Articles

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

Sort by
Same author

De Novo Heart Failure in a 32-Year-Old Man From Congenital Ostial Left Main Coronary Atresia.

CJC pediatric and congenital heart disease·2026
Same author

Association Between Preoperative Iron Deficiency and Postoperative Outcomes in Children Undergoing Cardiac Surgery.

Journal of cardiothoracic and vascular anesthesia·2026
Same author

Cardioplegia practices in pediatric cardiovascular surgery: Survey across Asia, Europe, and North America.

JTCVS open·2026
Same author

Toward precision in simulation of paediatric mitral valve repair using patient-specific fluid-structure interaction modelling.

European heart journal. Imaging methods and practice·2026
Same author

Durability and Elongation of Artificial Chords in Pediatric Mitral Valve Repair: A Comprehensive Echocardiographic Analysis.

The Journal of thoracic and cardiovascular surgery·2026
Same author

Elevations in Donor-Derived Cell-Free DNA and Allograft Outcomes in Kidney Transplantation.

Journal of the American Society of Nephrology : JASN·2026

Related Experiment Video

Updated: Sep 16, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

3.7K

Quantifying random variability in decision-making in pediatric cardiac surgery.

Kayla V Dlugos1,2,3, Mjaye Mazwi4, Marisa Signorile5

  • 1Institute of Medical Science, University of Toronto, Ontario, Canada.

JTCVS Open
|July 9, 2025
PubMed
Summary
This summary is machine-generated.

Medical decision-making exhibits significant "noise," or random variability, impacting quality and reproducibility. A noise audit quantified this variability in congenital heart disease care, revealing pervasive noise across different experience levels and roles.

Keywords:
congenital heart surgery entropymedical decision-makingnoisenoise auditrandom variability

More Related Videos

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.5K

Related Experiment Videos

Last Updated: Sep 16, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

3.7K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.5K

Area of Science:

  • Medical Decision Making
  • Healthcare Quality Improvement
  • Biostatistics

Background:

  • Random variability, termed "noise," negatively affects decision-making reproducibility and quality in various fields.
  • The prevalence and impact of noise in medical decision-making remain understudied.
  • Understanding and quantifying noise are crucial for enhancing patient care quality and decision consistency.

Purpose of the Study:

  • To quantify the extent of noise in medical decision-making within pediatric cardiology.
  • To assess how noise varies across different levels of question complexity and professional experience.
  • To establish a quantifiable method for measuring decision-making variability in clinical practice.

Main Methods:

  • A noise audit was conducted with 71 healthcare professionals at two children's hospitals.
  • Participants responded to case-based questions related to congenital heart disease (transposition of the great arteries, critical aortic stenosis).
  • Entropy was used to quantify response variation, with standardized entropy and aggregate entropy ratios calculated to compare groups and question complexities.

Main Results:

  • An overall aggregate entropy ratio of 0.85 indicated less variation in simpler questions compared to complex ones.
  • Professionals with over 10 years of experience showed slightly less noise (ratio 0.8) than those with less experience (ratio 0.87).
  • Aggregate entropy ratios varied by role: cardiac critical care (0.85), cardiology (0.83), and cardiovascular surgery (0.96).

Conclusions:

  • Noise is a pervasive factor in medical decision-making for congenital heart disease.
  • The noise audit successfully quantified decision-making variability, enabling comparative analysis.
  • Findings highlight the need for strategies to mitigate noise and improve consistency in clinical judgments.