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Related Concept Videos

Methods of reducing fever

Methods of reducing fever

The signs and symptoms of fever include hot and dry skin, flushed face, thirst, muscle aches, anorexia, headache, tachycardia, tachypnea, and fatigue. Elevated body temperature is reduced using two methods: pharmacological and nonpharmacological. Proper identification and treatment of the root cause of a fever is of utmost importance.
Pharmacological Methods of Reducing Fever:

Increased Body Temperature

Increased Body Temperature

A body temperature above 38°C (100.4 °F) is known as fever or pyrexia, and a person with fever is termed 'febrile.' Typically, the hypothalamus, a part of the brain that acts as the body's thermostat, regulates body temperature through a thermoregulatory setpoint. It receives signals from cold and warm thermal receptors throughout the body and adjusts the body's temperature accordingly. Fever occurs when this hypothalamic setpoint is altered, usually in response to an infection or illness.

Burn Injuries

Burn Injuries

Burn injuries occur when the skin and underlying tissues are damaged due to exposure to heat, electricity, chemicals, radiation, or friction. They can vary in severity, from minor superficial burns to severe deep burns that can be life-threatening.
The damage results in the death of skin cells, which can lead to a massive loss of fluid. Dehydration, electrolyte imbalance, and renal and circulatory failure follow, which can be fatal. Burn patients are treated with intravenous fluids to offset...

Types of Fever

Types of Fever

Fever can be triggered by several factors, including infections, nervous system disorders, certain cancers, blood diseases like leukemia, embolism, thrombosis, heatstroke, dehydration, surgical trauma, crushing injuries, and allergic reactions.
Here are the different types of fever:

Patterns of Fever

Patterns of Fever

Before understanding the types and patterns of fever, it is essential to know its phases.

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Related Experiment Videos

Machine Learning-Based Predictive Model for Fever and Adverse Clinical Events in Hospitalized Pediatric Burn

Lior Har-Shai^1,2, Sapir Gershov³, Tomer Lagziel^1,2,4

¹Division of Pediatric Plastic Surgery and Burns, Rabin Medical Center - Schneider Children's Hospital, Petach Tikva, Israel.

Journal of Burn Care & Research : Official Publication of the American Burn Association

|May 8, 2026

Summary

This summary is machine-generated.

Machine learning models accurately predict fever and adverse outcomes in pediatric burn patients. This tool aids early risk stratification, improving patient monitoring and resource allocation for better care.

Related Experiment Videos

Area of Science:

Pediatric Burn Injury
Machine Learning in Medicine
Clinical Risk Stratification

Background:

Systemic inflammation post-pediatric burn injury often causes fever, masking infections.
Accurate risk stratification is crucial for identifying hospitalized pediatric burn patients at risk of adverse events.

Purpose of the Study:

To develop and validate a machine learning (ML) model using Random Forest (RF) to predict fever and related adverse outcomes in pediatric burn patients.
To enhance early identification of high-risk patients for improved clinical management.

Main Methods:

Retrospective analysis of 595 pediatric burn patients (2012-2022).
Developed RF models to predict fever, pediatric intensive care unit (PICU) transfer, and surgical intervention.
Utilized multiple imputation and bootstrap sampling to handle missing data and class imbalance.

Main Results:

RF models achieved high predictive accuracy: F1-scores of 0.81 (fever), 0.88 (PICU transfer), 0.81 (surgery).
Area Under the Curve (AUC) values were excellent (0.95-0.97).
Key predictors included younger age, lower body weight, female sex, and head/neck burns.

Conclusions:

ML-based RF models show significant potential for early risk stratification in pediatric burn patients.
These models can guide monitoring intensity, diagnostic vigilance, and resource planning.
Prospective studies are needed to confirm outcome improvements with model-informed workflows.