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Related Concept Videos

Numerical Calculations01:24

Numerical Calculations

In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
The solution to a problem is obtained using different methods. While manually solving algebraic symbols is one of the most common methods, the graphical method is often preferred. Computers...
Percentile01:18

Percentile

A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile. Low percentiles always correspond to lower data values. High percentiles always correspond to higher data values.Percentiles divide ordered data into hundredths. To score in the...
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Ratio Level of Measurement00:54

Ratio Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...
Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...

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Related Experiment Video

Updated: Jun 15, 2026

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)
10:58

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Published on: August 28, 2021

Numerical scoring for the BILAG-2004 index.

Chee-Seng Yee1, Lynne Cresswell, Vernon Farewell

  • 1Rheumatology Research Group, School of Immunity and Infection, College of Medical and Dental Sciences, The Medical School, University of Birmingham, Birmingham B15 2TT, UK. csyee@ymail.com

Rheumatology (Oxford, England)
|February 26, 2010
PubMed
Summary
This summary is machine-generated.

A new scoring system for the British Isles Lupus Assessment Group (BILAG)-2004 index was developed. The recommended numerical scoring scheme is A=12, B=8, C=1, and D/E=0 for SLE disease activity assessment.

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Area of Science:

  • Rheumatology
  • Systemic Lupus Erythematosus (SLE)
  • Clinical Assessment Tools

Background:

  • The BILAG-2004 index is a crucial tool for assessing SLE disease activity.
  • Existing scoring methods may require refinement for accurate clinical application.

Purpose of the Study:

  • To develop and validate an additive numerical scoring scheme for the BILAG-2004 index.
  • To establish a more precise quantitative measure of SLE disease activity.

Main Methods:

  • Multi-center cross-sectional study involving SLE patients.
  • Logistic regression analysis to model disease activity based on treatment escalation.
  • Comparison of different scoring schemes to determine optimal numerical values.

Main Results:

  • Analysis of 1510 assessments from 369 SLE patients.
  • Previous scoring schemes for the BILAG index showed poor data fit.
  • A new scheme (A=12, B=8, C=1, D/E=0) demonstrated better consistency and fit.

Conclusions:

  • A validated additive numerical scoring scheme for the BILAG-2004 index has been established.
  • The proposed scheme (A=12, B=8, C=1, D/E=0) is recommended for clinical use.
  • This scoring system aids in objective assessment of SLE disease activity based on treatment decisions.