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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
An...
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Practical tool to identify Spasticity-Plus Syndrome amongst patients with multiple sclerosis. Algorithm development

Óscar Fernández Fernández1, Lucienne Costa-Frossard2, Maria Luisa Martínez Ginés3

  • 1Department of Pharmacology, Faculty of Medicine, Institute of Biomedical Research of Malaga (IBIMA), University of Malaga, Málaga, Spain.

Frontiers in Neurology
|May 6, 2024
PubMed
Summary

A new tool helps neurologists quickly identify Spasticity-Plus Syndrome (SPS) in multiple sclerosis (MS) patients. This algorithm aids in early detection, potentially simplifying treatment and reducing side effects.

Keywords:
Spasticity-Plus Syndromebladder dysfunctionconjoint analysismultiple sclerosisnabiximolsspasticity

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Area of Science:

  • Neurology
  • Clinical Research
  • Medical Diagnostics

Background:

  • Spasticity-Plus Syndrome (SPS) in multiple sclerosis (MS) involves spasticity plus other symptoms like spasms, tremor, and fatigue.
  • Early detection of SPS is crucial for effective patient management.

Purpose of the Study:

  • To develop a user-friendly algorithmic tool for the early detection of Spasticity-Plus Syndrome in multiple sclerosis patients.

Main Methods:

  • A conjoint analysis survey was conducted with 72 neurologists evaluating 12 patient profiles.
  • Logistic regression modeled the contribution of eight key SPS signs/symptoms to classify patients.

Main Results:

  • Spasticity, spasms, tremor, cramps, and bladder dysfunction were identified as key indicators of SPS.
  • The logistic regression model demonstrated good fit (AUC=0.816) and strong concordance with expert evaluations.
  • The tool provides a probability score for SPS, categorized as high (>60%), moderate (30-60%), or low (<30%).

Conclusions:

  • An algorithmic tool has been developed to assist healthcare professionals in identifying SPS in MS patients.
  • This tool can simplify SPS management and potentially reduce polypharmacotherapy side effects.