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Related Concept Videos

Decreased Body Temperature01:29

Decreased Body Temperature

827
A decreased body temperature can occur in patients with hypothermia and frostbite. Heat loss with extended cold exposure overpowers the body's ability to create heat, resulting in hypothermia. Core temperature readings help classify hypothermia. Mild hypothermia is temperatures between 32 °C (89.6 °F) and 35°C (95 °F) and is caused by impaired thermoregulation. Moderate hypothermia is temperatures between 28 C (82.4 °F) and 32 °C (89.6 °F) caused by...
827
Factors Affecting Body Temperature01:28

Factors Affecting Body Temperature

8.0K
As a nurse, it is vital to understand the factors affecting body temperature to monitor variations and effectively evaluate deviations from regular.
Factors may  include:
8.0K
Assessing Body Temperature - Axilla01:14

Assessing Body Temperature - Axilla

931
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...
931
Methods of reducing fever01:22

Methods of reducing fever

995
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:
995
Assessing Body Temperature - Temporal Artery01:19

Assessing Body Temperature - Temporal Artery

834
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...
834
Temperature Measurement Sites01:14

Temperature Measurement Sites

2.7K
A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
2.7K

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Related Experiment Video

Updated: Nov 22, 2025

Short-Duration Hypothermia Induction in Rats using Models for Studies examining Clinical Relevance and Mechanisms
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Short-Duration Hypothermia Induction in Rats using Models for Studies examining Clinical Relevance and Mechanisms

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Machine learning-based prediction models for accidental hypothermia patients.

Yohei Okada1,2,3, Tasuku Matsuyama4, Sachiko Morita5

  • 1Department of Primary Care and Emergency Medicine, Graduate School of Medicine, Kyoto University, ShogoinKawaramachi54, Sakyo, Kyoto, 606-8507, Japan. yokada-kyf@umin.ac.jp.

Journal of Intensive Care
|January 10, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict in-hospital mortality in accidental hypothermia patients using readily available data. These tools can aid clinical decision-making, though further prospective studies are needed to confirm clinical utility.

Keywords:
Accidental hypothermiaArtificial intelligenceGradient boosting treeLassoMachine learningPredictionRandom forest

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Area of Science:

  • Medical Informatics
  • Critical Care Medicine
  • Machine Learning

Background:

  • Accidental hypothermia presents significant mortality risks, including fatal arrhythmia and organ failure.
  • No established models exist for predicting mortality in accidental hypothermia patients.
  • This study developed machine learning models to predict in-hospital mortality using admission data.

Purpose of the Study:

  • To develop and validate machine learning-based models for predicting in-hospital mortality in accidental hypothermia patients.
  • To utilize easily accessible hospital admission data for predictive modeling.
  • To compare the performance of machine learning models against existing scoring systems.

Main Methods:

  • Secondary analysis of a multi-center retrospective cohort study (J-point registry).
  • Included adult patients with a body temperature of 35.0 °C or less upon emergency department arrival.
  • Developed and validated machine learning models (lasso, random forest, gradient boosting tree) using data from development and validation cohorts.

Main Results:

  • The study included 532 patients across development (22.0% mortality) and validation (27.0% mortality) cohorts.
  • Machine learning models demonstrated strong predictive performance in the validation cohort, with C-statistics ranging from 0.780 to 0.794.
  • Models showed good calibration and clinical net-benefit compared to SOFA and 5A scores.

Conclusions:

  • Machine learning models can accurately predict in-hospital mortality in accidental hypothermia patients.
  • These models show potential to support clinical decision-making.
  • Further prospective studies are required to establish clinical applicability and usefulness.