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Related Concept Videos

Increased Body Temperature01:25

Increased Body Temperature

2.8K
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...
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Patterns of Fever01:26

Patterns of Fever

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Before understanding the types and patterns of fever, it is essential to know its phases.
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Types of Fever01:25

Types of Fever

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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:
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Methods of reducing fever01:22

Methods of reducing fever

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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:
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Assessing Body Temperature - Temporal Artery01:19

Assessing Body Temperature - Temporal Artery

699
Here is a stepwise guide to assessing the body temperature at the temporal artery using a temporal artery thermometer
Step 1: Perform hand hygiene and don a fresh pair of gloves to prevent cross-infection and ensure patient safety.
Step 2: Explain the procedure to the patient to establish trust. Clear communication establishes trust with the patient, ensures they understand what to expect, promotes cooperation, and enhances comfort during the procedure.  
Step 3: Assess the patient's...
699
Assessing Body Temperature - Axilla01:14

Assessing Body Temperature - Axilla

685
Procedural Guide for Assessing Axillary Body Temperature using a Digital Thermometer:
Step 1: Perform hand hygiene and put on clean gloves to maintain infection control and prevent cross-contamination.
Step 2: Prepare the patient by explaining the procedure to ensure understanding and cooperation. Ensure privacy, expose the axilla, and inform the patient that minimal movement is crucial for an accurate reading.
Step 3: Adjust the patient’s clothing to expose only the axilla. It minimizes...
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Related Experiment Video

Updated: Sep 26, 2025

Mouse Body Temperature Measurement Using Infrared Thermometer During Passive Systemic Anaphylaxis and Food Allergy Evaluation
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Mouse Body Temperature Measurement Using Infrared Thermometer During Passive Systemic Anaphylaxis and Food Allergy Evaluation

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Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis.

Borja Vargas1, David Cuesta-Frau2, Paula González-López1

  • 1Department of Internal Medicine, Hospital Universitario de Móstoles, 28935 Mostoles, Spain.

Entropy (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

Analyzing body temperature time series using entropy methods like Slope Entropy can help differentiate bacterial infections from other fever causes. Slope Entropy shows promise for sensitive and specific early fever diagnosis.

Keywords:
Approximate EntropySample EntropySlope Entropybody temperatureclassificationfevertime series

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Area of Science:

  • Biomedical Engineering
  • Clinical Thermometry
  • Data Analysis

Background:

  • Traditional fever detection relies on binary thresholds.
  • Continuous body temperature time series analysis offers advanced diagnostic potential.
  • Entropy measures provide insights into time series dynamics.

Purpose of the Study:

  • To compare three time series entropy methods (Slope Entropy, Approximate Entropy, Sample Entropy) for analyzing body temperature.
  • To identify differences in body temperature patterns between bacterial infections and other fever causes.
  • To evaluate the potential for automatic classification of fever types.

Main Methods:

  • Application of Slope Entropy, Approximate Entropy, and Sample Entropy to patient body temperature records.
  • Analysis of time series data from patients with bacterial infections and other fever causes.
  • Comparative analysis of the performance of the entropy calculation methods.

Main Results:

  • Slope Entropy demonstrated stability and robustness in analyzing body temperature time series.
  • This method identified statistically significant differences between patient groups.
  • Sensitivity and specificity exceeded 70% in most experimental cases, highlighting its diagnostic potential.

Conclusions:

  • Slope Entropy is a promising tool for enhancing entropy-based analysis in clinical thermometry.
  • This method offers improved sensitivity for early fever diagnosis and classification.
  • The findings support the use of Slope Entropy for automatic classification of fever etiologies.