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Related Concept Videos

Correlation and Regression00:53

Correlation and Regression

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 negative...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
Coefficient of Correlation01:12

Coefficient of Correlation

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.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...

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Related Experiment Video

Updated: Jun 8, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

An evaluation of the Cariogram as a predictor model.

Devinder Utreja1, Mauli Simratvir, Avninder Kaur

  • 1Dept. of Pedodontics and Preventive Dentistry, B.R.S. Dental College, Panchkulla, Haryana, India. queenraj82@yahoo.com

International Dental Journal
|October 19, 2010
PubMed
Summary

The Cariogram

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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

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Area of Science:

  • Pediatric Dentistry
  • Dental Caries Research
  • Diagnostic Accuracy Studies

Background:

  • The shift towards preventive dentistry necessitates improved diagnostic tools.
  • Early detection of dental caries is crucial for effective intervention.
  • Assessing the predictive capability of existing diagnostic aids is vital.

Purpose of the Study:

  • To evaluate the accuracy of the Cariogram in predicting caries development.
  • To assess the diagnostic performance of the Cariogram for first permanent molars.

Main Methods:

  • Thirty 8-year-old children were studied.
  • Participants were grouped based on the presence or absence of caries in first permanent molars.
  • Cariogram analysis was performed and compared with clinical findings.

Main Results:

  • The Cariogram demonstrated a diagnostic accuracy of 63.33% in predicting caries.
  • This accuracy level indicates limitations in its predictive value for this specific application.

Conclusions:

  • The Cariogram's current accuracy is insufficient for reliable caries prediction in first permanent molars.
  • There is a clear need for the development of more precise dental caries prediction models.