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

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

404
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
404
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

313
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
313
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

246
Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
246
Random Error01:04

Random Error

9.8K
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...
9.8K
Random Variables01:09

Random Variables

17.8K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.8K
Randomized Experiments01:13

Randomized Experiments

9.0K
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...
9.0K

You might also read

Related Articles

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

Sort by
Same author

Macrotroponin I: association with suspected myocardial infarction with non-obstructive coronary artery.

European heart journal·2026
Same author

Neostigmine Versus Sugammadex for Reversal of Neuromuscular Blockade in Elderly Patients: A Blinded Randomised Study.

Acta anaesthesiologica Scandinavica·2025
Same author

GDF-15 plasma levels are elevated in mobility-limited older adults with frailty and sarcopenia-results from the BIOFRAIL study.

GeroScience·2025
Same author

Estimation of Cardiorespiratory Fitness in Military Applicants Using Seismocardiography.

Military medicine·2025
Same author

Effects of Hyperoxia and Antioxidants on Mortality, Hospital Admissions, and Myocardial Infarction After Noncardiac Surgery: 1-Year Follow-Up of a Randomized Controlled Trial.

Acta anaesthesiologica Scandinavica·2025
Same author

Humoral Immune Response Following COVID-19 Vaccination in Multifocal Motor Neuropathy and Chronic Inflammatory Demyelinating Polyneuropathy.

Vaccines·2025

Related Experiment Video

Updated: Jan 29, 2026

Tissue Triage and Freezing for Models of Skeletal Muscle Disease
05:58

Tissue Triage and Freezing for Models of Skeletal Muscle Disease

Published on: July 15, 2014

41.3K

The Copenhagen Triage Algorithm is non-inferior to a traditional triage algorithm: A cluster-randomized study.

Rasmus Bo Hasselbalch1, Mia Pries-Heje1, Martin Schultz1

  • 1Department of Cardiology, Herlev-Gentofte Hospital, Copenhagen, Denmark.

Plos One
|February 5, 2019
PubMed
Summary

The Copenhagen Triage Algorithm (CTA) demonstrated non-inferiority to ADAPT in short-term mortality and superior 30-day mortality prediction. This vital signs and clinical assessment triage system offers improved accuracy for emergency departments.

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K

Related Experiment Videos

Last Updated: Jan 29, 2026

Tissue Triage and Freezing for Models of Skeletal Muscle Disease
05:58

Tissue Triage and Freezing for Models of Skeletal Muscle Disease

Published on: July 15, 2014

41.3K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K

Area of Science:

  • Emergency Medicine
  • Clinical Triage Systems
  • Health Services Research

Background:

  • Emergency departments (EDs) globally utilize triage systems with limited clinical judgment.
  • The Copenhagen Triage Algorithm (CTA) is a simplified system incorporating clinical assessment.

Purpose of the Study:

  • To compare the Copenhagen Triage Algorithm (CTA) against a local adaptation of the Adaptive Process Triage (ADAPT) system.
  • To evaluate the non-inferiority of CTA regarding short-term mortality and its predictive performance for 30-day mortality.

Main Methods:

  • A two-center, cluster-randomized crossover study involving 45,347 patient visits.
  • CTA, based on vital signs and nurse's clinical assessment, was compared to ADAPT.
  • Primary endpoint was 30-day mortality with a non-inferiority margin of 0.5%; predictive performance assessed via Receiver Operator Characteristics.

Main Results:

  • CTA met non-inferiority criteria for 30-day mortality (3.42% vs. 3.43%, P = 0.996).
  • CTA showed superior predictive performance for 30-day mortality (AUC 0.67 vs. 0.64, P = 0.03).
  • No significant differences observed in ICU admission, length of stay, waiting times, or readmission rates.

Conclusions:

  • The novel CTA, integrating vital signs and clinical assessment, is non-inferior to traditional triage algorithms for short-term mortality.
  • CTA demonstrates superior accuracy in predicting 30-day mortality in emergency department settings.