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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlation and Regression00:53

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Coefficient of Correlation01:12

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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Correlation of Experimental Data01:23

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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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Scleroderma with an update about clinico-pathological correlation.

Franco Rongioletti1, Caterina Ferreli2, Laura Atzori2

  • 1Dermatologic Clinic, Department of Public Health, University of Cagliari, Cagliari, Italy - rongioletti@unica.it.

Giornale Italiano Di Dermatologia E Venereologia : Organo Ufficiale, Societa Italiana Di Dermatologia E Sifilografia
|January 26, 2018
PubMed
Summary
This summary is machine-generated.

Scleroderma, encompassing systemic sclerosis and localized morphea, is diagnosed clinically. Skin biopsy is reserved for diagnostic uncertainty or research, aiding in understanding disease pathogenesis and differentiating subtypes.

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Area of Science:

  • Rheumatology
  • Dermatology
  • Pathology

Background:

  • Scleroderma presents as systemic sclerosis or localized morphea.
  • Current diagnostic criteria (ACR/EULAR 2013) rely on clinical signs like skin thickening.
  • Histological examination is not routine but valuable for research and differential diagnosis.

Purpose of the Study:

  • To review the role of skin biopsy in scleroderma diagnosis and research.
  • To highlight the histopathological changes in scleroderma.
  • To discuss the utility of histopathology in classifying localized scleroderma subtypes.

Main Methods:

  • Review of diagnostic criteria for scleroderma.
  • Examination of histopathological findings in localized and systemic scleroderma.
  • Discussion of indications for skin biopsy in scleroderma.

Main Results:

  • Skin biopsy is not part of standard scleroderma classification criteria.
  • Histopathology reveals microvascular changes, inflammation, and fibrosis.
  • Histological features can aid in differentiating scleroderma subtypes and similar conditions.

Conclusions:

  • Skin biopsy plays a crucial role in research for understanding scleroderma pathogenesis.
  • Histopathology is essential for diagnosing challenging cases and distinguishing between scleroderma variants.
  • Further research into histopathological differences may refine localized scleroderma classification.