Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Seizures: Classification

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:

Drug Toxicity: Risk factors

Drug Toxicity: Risk factors

Adverse Drug Reactions (ADRs) are potential complications that arise during pharmacotherapy, influenced by multiple risk factors. Age plays a significant role; both neonates and the elderly are at heightened risk due to their respective immature and diminished metabolic and elimination processes. Gender also impacts ADRs, with females experiencing a 1.5 to 1.7-fold greater risk than males, which may be linked to pharmacokinetic, pharmacodynamic, and hormonal differences. Notably, neonates, the...

Introduction to Test of Independence

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

Hypothesis Test for Test of Independence

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...

You might also read

Related Articles

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

Sort by

Same author

Changes in leukocytes and CRP in different treatments of major depression.

Journal of neural transmission (Vienna, Austria : 1996)·2026

Same author

Prenatal Alcohol Exposure Predicts Academic Outcomes from Childhood to Adolescence: A Prospective Longitudinal Study Based on Meconium Ethyl Glucuronide.

Behavioral sciences (Basel, Switzerland)·2026

Same author

Investigating Factors Associated With Spontaneous Remission in Individuals With Alcohol Use Disorder-Results From a Multi-Site Longitudinal Cohort Study.

Addiction biology·2026

Same author

Masculine depression and acute mental health burden.

Scientific reports·2026

Same author

Efficacy, moderators and mediators of cognitive behavioural analysis system of psychotherapy (CBASP) versus behavioural activation (BA) in persistently depressed treatment-resistant inpatients: study protocol for the multicentre, randomised controlled <i>changePDD</i> trial.

BMJ open·2026

Same author

Dissociating role of Bassoon in glutamatergic and dopaminergic neurons in alcohol-related behaviour and affective state in mice.

British journal of pharmacology·2026

Search research articles

Related Experiment Video

Updated: May 22, 2026

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm.

Thomas Hillemacher¹, Helge Frieling, Julia Wilhelm

¹Center for Addiction Research-CARe, Department for Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany. hillemacher.thomas@mh-hannover.de

Journal of Neural Transmission (Vienna, Austria : 1996)

|May 25, 2012

Summary

Predicting alcohol-withdrawal seizures (AWS) is crucial for alcohol-dependent patients. A random forest model identified key predictors including homocysteine, prolactin, and smoking habits, aiding in personalized risk assessment.

More Related Videos

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

Published on: April 28, 2022

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

Related Experiment Videos

Last Updated: May 22, 2026

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

Published on: April 28, 2022

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

Area of Science:

Neuroscience
Addiction Medicine
Clinical Toxicology

Background:

Alcohol-withdrawal seizures (AWS) are a significant complication in alcohol detoxification.
Accurate prediction of AWS risk is essential for patient management.
Identifying predictive markers can improve individualized treatment strategies.

Purpose of the Study:

To identify reliable predictors of alcohol-withdrawal seizures (AWS) in alcohol-dependent patients undergoing detoxification.
To evaluate the efficacy of a random forest algorithm in predicting AWS.
To establish a basis for further research into AWS prediction.

Main Methods:

A random forest algorithm was employed to analyze data from 200 alcohol-dependent patients.
Predictive markers for AWS were assessed using a machine learning approach.
Key variables included homocysteine, prolactin, blood alcohol concentration, withdrawal history, age, and smoking status.

Main Results:

The study identified a combination of factors that can successfully predict AWS.
Key predictors include homocysteine levels, prolactin levels, blood alcohol concentration on admission, prior withdrawal history, patient age, and smoking frequency.
The random forest model demonstrated potential for accurate AWS risk stratification.

Conclusions:

The findings suggest that a combination of biological, demographic, and behavioral factors can predict AWS.
Further research incorporating additional biological and psychosocial parameters is warranted.
This predictive model can inform clinical practice and personalized detoxification protocols for alcohol-dependent individuals.