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Related Concept Videos

Inequalities01:28

Inequalities

Inequalities express mathematical relationships where two values are not equal and are compared using symbols such as <, >, ≤, or ≥. These expressions define a range of possible solutions rather than a single value. Interval notation provides a concise way to express these solution sets, especially when the variable spans a continuous range. An open interval, written as (a, b), excludes the endpoints, while a closed interval [a, b] includes them. There are also half-open intervals, such...
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the key values are 3...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all points...
Absolute Value Inequalities01:23

Absolute Value Inequalities

The absolute value is a mathematical tool that represents the distance of a number from zero on the number line, regardless of its sign. In the context of inequalities, absolute value expressions help define a range of permissible values or boundaries for a variable. These inequalities are commonly used in scientific modeling and data interpretation, where variability within or beyond a certain threshold must be captured precisely.An absolute value inequality of the form ∣x∣ ≤ a, where a ≥ 0,...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

When is equal not equal?

Allan D Sniderman1, Ken Williams, Matthew J McQueen

  • 1Mike Rosenbloom Laboratory for Cardiovascular Research, McGill University Health Centre, Room H7.22, Royal Victoria Hospital, 687 Pine Avenue West, Montreal, Quebec H3A 1A1, Canada. allansniderman@hotmail.com

Journal of Clinical Lipidology
|December 3, 2010
PubMed
Summary
This summary is machine-generated.

Major cholesterol markers and apolipoproteins are not interchangeable for predicting individual vascular disease risk. ApoB and non-HDL cholesterol levels can diverge, leading to different risk assessments for individuals.

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Area of Science:

  • Cardiovascular Medicine
  • Biomarkers
  • Risk Assessment

Background:

  • Emerging Risk Factor Collaboration meta-analysis suggested functional interchangeability of cholesterol markers and apolipoproteins for vascular disease risk.
  • Conventional hazard ratios (HR) assess group-level risk per standard deviation, potentially overlooking individual variability.

Purpose of the Study:

  • To investigate limitations in the interpretation of cholesterol and apolipoprotein HR for individual vascular disease risk assessment.
  • To demonstrate the common discordance between non-high-density lipoprotein cholesterol and apolipoprotein B (apoB) in predicting individual risk.

Main Methods:

  • Analysis of hazard ratios (HR) for vascular disease associated with cholesterol markers and apolipoproteins.
  • Evaluation of individual-level risk prediction based on concordant and discordant levels of non-HDL cholesterol and apoB.

Main Results:

  • Substantial discordance between non-high-density lipoprotein cholesterol and apoB in predicting individual vascular disease risk is common.
  • Even with similar group HR, apoB indicates higher risk in some individuals, while non-HDL cholesterol does in others.

Conclusions:

  • Cholesterol markers and apolipoproteins are not functionally interchangeable for assessing individual vascular disease risk.
  • Focus should shift to combining information from multiple parameters for accurate individual risk evaluation, rather than group-level generalizations.