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

Professional Values01:29

Professional Values

10.6K
Nurses are responsible for caring for patients during birth, death, illness, and healing. Professional values guide the decisions and actions that nurses make in their careers. If nurses know the decisions and actions to take, providing patients with exceptional care is possible.
The values that are the foundation of the nursing profession are altruism, autonomy, human dignity, and social justice.
First, altruism refers to the concern for the welfare and well-being of others without personal...
10.6K
The Professional Nurse01:22

The Professional Nurse

6.5K
Professional nurses are not limited to bedside care and are taking roles of greater responsibility. A nurse should have a knowledge-based practice, including personal, theoretical, procedural, cultural, and reflexive knowledge. Additionally, nurses must be competent in cognitive, technical, interpersonal, and ethical/legal skills. Some of the best attributes of successful nurses include the following:
Communication skills: These are critical characteristics, especially speaking and listening.
6.5K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

303
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...
303
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

299
Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
299
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

2.1K
The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
2.1K
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

2.8K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Match running performance upon return to play in professional male LaLiga football players following anterior cruciate ligament rupture.

Biology of sport·2026
Same author

Amylase/trypsin inhibitors (ATIs) levels in wheat event IND-ØØ412-7 are similar to non-transgenic wheat.

Transgenic research·2026
Same author

Mechanisms, Injury Patterns and Biomechanical Factors of Anterior Cruciate Ligament Injuries in Football (Soccer): A Systematic Review and Meta-Analysis of Video-Analysis Studies.

Sports medicine (Auckland, N.Z.)·2026
Same author

Implementing an Evidence-Based Neonatal Circumcision Pain Management Protocol: A Quality Improvement Initiative.

Journal of pediatric health care : official publication of National Association of Pediatric Nurse Associates & Practitioners·2025
Same author

Spanish adaptation of the cutting movement assessment score (CMAS): Agreement and inter-rater reliability among Spanish-speaking football practitioners.

Physical therapy in sport : official journal of the Association of Chartered Physiotherapists in Sports Medicine·2025
Same author

Systematic video analysis of ACL tear patterns in Spanish professional female football players: Neurocognitive errors as targets for prevention.

Journal of science and medicine in sport·2025

Related Experiment Video

Updated: Jan 27, 2026

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

7.4K

A Preventive Model for Hamstring Injuries in Professional Soccer: Learning Algorithms.

Francisco Ayala1, Alejandro López-Valenciano1, Jose Antonio Gámez Martín2

  • 1Department of Sport Science, Sport Research Centre, Miguel Hernández University of Elche, Elche (Alicante), Spain.

International Journal of Sports Medicine
|March 16, 2019
PubMed
Summary

Machine learning models can predict hamstring strain injuries (HSI) in soccer players. A specific technique identified players at high risk, aiding injury prevention strategies.

More Related Videos

Effects of a Novel Neuromuscular Training Intervention on Jump, Sprint, and Change of Direction in Adult Female Soccer Players
10:08

Effects of a Novel Neuromuscular Training Intervention on Jump, Sprint, and Change of Direction in Adult Female Soccer Players

Published on: June 10, 2025

1.3K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Related Experiment Videos

Last Updated: Jan 27, 2026

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

7.4K
Effects of a Novel Neuromuscular Training Intervention on Jump, Sprint, and Change of Direction in Adult Female Soccer Players
10:08

Effects of a Novel Neuromuscular Training Intervention on Jump, Sprint, and Change of Direction in Adult Female Soccer Players

Published on: June 10, 2025

1.3K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Area of Science:

  • Sports Medicine
  • Data Science
  • Biomechanics

Background:

  • Hamstring strain injury (HSI) is a common and significant injury in professional soccer.
  • Identifying players at high risk of HSI is crucial for effective injury prevention.

Purpose of the Study:

  • To compare machine learning techniques for predicting HSI risk factors.
  • To identify the best predictive model for HSI in professional soccer players.

Main Methods:

  • 96 male professional soccer players underwent pre-season screening including individual, psychological, and neuromuscular assessments.
  • Injury surveillance prospectively recorded all HSIs during the 2013/2014 season.
  • Various machine learning techniques were analyzed to determine predictive accuracy.

Main Results:

  • The SmooteBoostM1 technique with a cost-sensitive ADTree classifier demonstrated the best performance (AUC=0.837, TPR=77.8%, TNR=83.8%).
  • The developed model showed moderate to high accuracy in identifying players at risk for HSI.
  • Most HSIs occurred in the dominant leg (55.6%) compared to the non-dominant leg (44.4%).

Conclusions:

  • A machine learning model can effectively predict hamstring strain injuries in professional soccer players.
  • This predictive model can assist coaches and medical staff in making informed decisions for injury prevention.
  • Pre-season screening combined with advanced analytics offers a promising approach to mitigate HSI risk.