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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

8.1K
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
An...
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Structural Classification of Joints01:20

Structural Classification of Joints

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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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Related Experiment Video

Updated: May 2, 2026

Evaluation of Patients' Posture and Gait Profile After Lumbar Fusion Surgery by Video Rasterstereography and Treadmill Gait Analysis
07:44

Evaluation of Patients' Posture and Gait Profile After Lumbar Fusion Surgery by Video Rasterstereography and Treadmill Gait Analysis

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Analyzing Large Language Models' Responses to Common Lumbar Spine Fusion Surgery Questions: A Comparison Between

Siegmund Philipp Lang1,2, Ezra Tilahun Yoseph1, Aneysis D Gonzalez-Suarez1

  • 1Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.

Neurospine
|July 2, 2024
PubMed
Summary

Large language models like ChatGPT and Bard effectively answer patient questions about lumbar spine fusion surgery. However, further research is needed to optimize their role in patient education and healthcare communication.

Keywords:
Artificial intelligenceBardChatGPTLarge language modelsLumbar spine fusionPatient education

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Spine Surgery Patient Education

Background:

  • Patients increasingly seek health information online, including details on lumbar spine fusion.
  • Large language models (LLMs) offer potential for patient education but require evaluation.

Purpose of the Study:

  • To assess the quality of responses provided by ChatGPT 3.5 and Google Bard to patient queries regarding lumbar spine fusion surgery.
  • To compare the performance of two leading LLMs in delivering accurate and understandable information on spinal fusion procedures.

Main Methods:

  • Ten critical questions on lumbar spine fusion were selected from a larger set of frequently asked questions.
  • Responses from ChatGPT 3.5 and Bard were evaluated by five blinded spine surgeons using a 4-point rating scale.
  • Clarity and professionalism of the responses were assessed using a 5-point Likert scale.

Main Results:

  • Nearly all responses (97%) from both LLMs were rated as excellent or satisfactory.
  • ChatGPT 3.5 and Bard demonstrated comparable performance, with 62% and 66% excellent ratings, respectively.
  • Both models showed limitations in addressing specific questions about surgical risks, success rates, and approach selection, with low interrater reliability observed.

Conclusions:

  • ChatGPT 3.5 and Bard can effectively answer frequently asked questions about lumbar spine fusion.
  • Further development and validation are necessary to fully integrate LLMs into medical education and patient communication strategies.
  • LLMs show promise but require refinement to ensure consistent accuracy and address complex surgical queries comprehensively.