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Related Concept Videos

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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Anchoring Junctions01:03

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Anchoring junctions are multiprotein complexes that help cells connect to other cells and the extracellular matrix. Anchoring junctions are present on the lateral and basal surfaces of cells, providing strong and flexible connections. Focal adhesions are often formed due to cell interactions with the ECM substrata, which initiate signal transduction via kinase cascades and other mechanisms. Together, they provide stability and tissue integrity. There are three types of anchoring junctions:...
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Design Example: Measuring Distance Between Two Points with Obstructions01:10

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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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Method of Joints: Problem Solving II01:30

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Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
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Functional Classification of Joints01:09

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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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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Landmarking 2.0: Bridging the gap between joint models and landmarking.

Hein Putter1, Hans C van Houwelingen1

  • 1Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

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This study enhances landmarking for dynamic prediction using biomarkers. The improved method increases predictive accuracy while maintaining simplicity and robustness for time-dependent covariates.

Keywords:
joint modelslandmarkinglongitudinal biomarkerssurvival prediction

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Longitudinal Data Analysis

Background:

  • Dynamic prediction with time-dependent covariates (biomarkers) is crucial in medical research.
  • Existing methods include joint modeling and landmarking, each with limitations in efficiency and robustness.
  • Joint models can be inefficient if misspecified, while landmarking may be less efficient than correctly specified joint models.

Purpose of the Study:

  • To develop methods improving the predictive accuracy of landmarking for time-dependent covariates.
  • To retain the relative simplicity and robustness of the landmarking approach.
  • To provide reliable dynamic predictions for individual patient outcomes.

Main Methods:

  • Fitting a working longitudinal model for the biomarker, incorporating temporal correlation.
  • Deriving a predictable time-dependent process for the biomarker's expected value post-landmark.
  • Fitting a time-dependent Cox model using the derived predictable time-dependent covariate.

Main Results:

  • The proposed method enhances the predictive accuracy of landmarking.
  • Dynamic predictions are obtained by estimating biomarker trajectories and using a time-dependent Cox model.
  • The approach was illustrated for predicting overall survival in liver cirrhosis patients using prothrombin index.

Conclusions:

  • The developed method offers improved predictive accuracy for landmarking with time-dependent covariates.
  • This approach balances predictive performance with the inherent simplicity and robustness of landmarking.
  • It provides a valuable tool for dynamic prediction in clinical settings, exemplified by liver cirrhosis survival prediction.