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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.
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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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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In any system of units, the units for some physical quantities must be specified through a measurement process. These measurements are the base quantities of the system, and their units are the base units of the system. The algebraic combinations of the base values can then be used to express all other physical quantities. Each of these physical quantities is then referred to as a derived quantity, with each unit being referred to as a derived unit.
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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Joint Imputation of General Data.

Michael W Robbins1

  • 1Senior Statistician with the RAND Corporation, Pittsburgh, PA 15213, USA.

Journal of Survey Statistics and Methodology
|January 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for imputing missing data in complex surveys. The method ensures accurate and stable imputations, outperforming existing techniques like Fully Conditional Specification (FCS).

Keywords:
Fully conditional specificationJoint modelingMarkov Chain Monte CarloMissing dataMultiple imputation

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Area of Science:

  • Statistics
  • Data Science
  • Survey Methodology

Background:

  • High-dimensional complex survey data present challenges for missing data imputation.
  • Fully Conditional Specification (FCS) is a common but flawed imputation method for such data.
  • Existing joint modeling imputation methods lack the flexibility for general data structures.

Purpose of the Study:

  • To develop a novel algorithm for flexible and efficient multiple imputation by joint modeling.
  • To address the limitations of existing imputation methods for high-dimensional, complex survey data.
  • To provide a robust imputation solution for datasets like the Health-Related Behaviors Survey (HRBS).

Main Methods:

  • Developed an algorithm applying multiple imputation by joint modeling to general data structures.
  • Utilized a latent joint multivariate normal model underpinning the data.
  • Modeled latent data through user-specified conditional linear models.

Main Results:

  • The new algorithm produces convergent and high-quality imputations for HRBS data.
  • Rigorous evaluations demonstrate the algorithm's effectiveness.
  • Simulations show the proposed method outperforms existing algorithms, including FCS.

Conclusions:

  • The proposed joint modeling algorithm offers an efficient and flexible solution for imputing missing data in complex, high-dimensional surveys.
  • This method overcomes the theoretical flaws and limitations of previous approaches.
  • The algorithm is suitable for general data structures and demonstrates superior performance compared to FCS.