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Related Concept Videos

Assessment of Respiration01:23

Assessment of Respiration

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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
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Respiratory Assessment: Purpose and Indications01:19

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Respiratory assessment is a cornerstone of nursing assessments, crucial for the early detection of patient deterioration. This evaluation transcends routine procedures, representing a critical skill nurses must master to ensure optimal patient care.
Objectives and Importance:
The primary goal of respiratory assessment is to evaluate patients at early risk of clinical deterioration. Since respiratory distress often precedes other signs of declining health, breathing patterns and sounds become a...
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Physical Assessment of the Respiratory Tract I: Health History01:28

Physical Assessment of the Respiratory Tract I: Health History

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Physical assessment of the respiratory tract is critical to patient care. It allows healthcare professionals to identify and manage various respiratory conditions. The process involves a combination of subjective and objective data collection.
Subjective Data
Subjective data provides vital information about the patient's health history and symptoms. This data is typically collected through interviews in which patients describe their experiences, symptoms, and concerns.
Health history and...
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Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

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Nursing Assessment01:29

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The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
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Data Collection III01:05

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Updated: Feb 24, 2026

Oral Health Assessment by Lay Personnel for Older Adults
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AI-Induced Occupational Health Assessment.

Mathijs A Langezaal1,2, Marcel Goldberg1, Grégoire Rey3

  • 1Population-Based Epidemiological Cohorts Unit UMS11, INSERM, France.

Studies in Health Technology and Informatics
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

Occupational health studies use Job-Exposure Matrices (JEMs) to assess job-related risks. Large Language Models (LLMs) show promise for improving automatic occupational coding (AOC) accuracy and generalizability.

Keywords:
Artificial IntelligenceDecision Support Systems (DSS)EpidemiologyExposure AssessmentGeneralizationLarge Language Models (LLM)Occupation coding

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Area of Science:

  • Occupational Health
  • Epidemiology
  • Computational Linguistics

Background:

  • Work is vital for well-being but poses health risks.
  • Job-Exposure Matrices (JEMs) link job histories to health risk assessments.
  • Standardizing job descriptions for JEMs is manual, costly, and time-consuming.

Purpose of the Study:

  • To provide an overview of occupational health assessment and Automatic Occupational Coding (AOC).
  • To explore the potential of Large Language Models (LLMs) in AOC.
  • To address limitations in current AOC methods, including generalizability.

Main Methods:

  • Review of JEMs and occupational classification systems.
  • Analysis of existing (semi-)automatic coding and Decision Support Systems (DSS).
  • Exploration of Large Language Models (LLMs) for AOC applications.

Main Results:

  • Current fully automatic coding systems struggle with out-of-distribution data, limiting real-world use.
  • Decision Support Systems (DSS) improve reliability through expert correction.
  • LLMs offer potential for enhanced accuracy and generalizability in AOC.

Conclusions:

  • LLM application in AOC is currently unexplored.
  • LLMs could significantly improve the accuracy and generalizability of AOC.
  • Future research should focus on LLM integration into AOC systems.