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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Structural Classification of Joints01:20

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Joints01:26

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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
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Clearance Models: Noncompartmental Models01:17

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Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
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Ankle Joint01:10

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The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...
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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Related Experiment Video

Updated: Oct 22, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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A Discrete Joint Model for Entity and Relation Extraction from Clinical Notes.

Zongcheng Ji1, Omid Ghiasvand1, Stephen Wu1

  • 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 30, 2021
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Summary
This summary is machine-generated.

This study introduces a novel joint model for clinical natural language processing, improving the extraction of clinical concepts and relations from electronic health records by overcoming limitations of traditional pipeline methods.

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Area of Science:

  • Clinical Natural Language Processing
  • Biomedical Informatics

Background:

  • Extracting clinical concepts and relations from clinical narratives is crucial for clinical NLP.
  • Traditional methods use a pipeline architecture, separating Named Entity Recognition (NER) and Relation Classification (RC).

Purpose of the Study:

  • To address limitations of pipeline architectures in clinical NLP, such as error propagation and lack of inter-step interaction utilization.
  • To investigate a discrete joint model for simultaneous NER and RC from clinical notes.

Main Methods:

  • Developed and investigated a discrete joint model.
  • Utilized structured perceptron and beam search algorithms.
  • Applied the model to extract clinical concepts and their relations from clinical notes.

Main Results:

  • The joint model effectively performs Named Entity Recognition (NER) and Relation Classification (RC) simultaneously.
  • This approach mitigates error propagation inherent in pipeline methods.
  • The model leverages interactions between NER and RC for improved performance.

Conclusions:

  • A discrete joint model offers a more robust solution for clinical concept and relation extraction compared to traditional pipelines.
  • This integrated approach enhances the accuracy and efficiency of processing clinical narratives.
  • The findings contribute to advancing clinical NLP tasks for better data utilization.