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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Related Experiment Video

Updated: May 1, 2026

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Published on: June 30, 2014

Multicriteria decision analysis methods with 1000Minds for developing systemic sclerosis classification criteria.

Sindhu R Johnson¹, Raymond P Naden², Jaap Fransen³

¹Division of Rheumatology, Department of Medicine, Toronto Western Hospital, University of Toronto, Ground Floor, East Wing, 399 Bathurst Street, Toronto, Ontario, Canada M5T 2S8; Division of Rheumatology, Department of Medicine, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

Journal of Clinical Epidemiology

|April 12, 2014

Summary

This summary is machine-generated.

Developing new classification criteria for systemic sclerosis (SSc) involved creating a sensible instrument and using forced-choice methods to reduce and weight criteria, improving expert agreement. This approach sets a standard for future classification development.

Keywords:

Classification criteria Conjoint analysis Decision analysis Forced-choice Scleroderma Sensibility Systemic sclerosis

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Area of Science:

Rheumatology
Medical Informatics
Clinical Epidemiology

Background:

Systemic sclerosis (SSc) classification criteria are evolving.
A need exists for a standardized instrument to collect case data for classification development.
Expert consensus and rigorous methodology are crucial for reliable classification criteria.

Purpose of the Study:

To develop and evaluate a sensible instrument for collating case data for SSc classification.
To reduce and weight classification criteria using forced-choice methods.
To assess expert agreement on SSc classification probabilities.

Main Methods:

A standardized instrument was tested for sensibility with 20 SSc cases.
Experts used forced-choice methods to rank, reduce, and weight criteria.
Intraclass correlation coefficients (ICCs) evaluated consistency in expert rankings.

Main Results:

The instrument demonstrated high face and content validity, clarity, and comprehensibility.
Key criteria were weighted, including friction rubs (21 points) and fingertip lesions (9-21 points).
Initial expert agreement (ICC) was 0.73, improving to 0.80 after applying the methods.

Conclusions:

A 39% reduction in criteria was achieved using a sensible instrument and forced-choice methods.
The developed methods offer a rigorous, measurement-science-based template for creating classification criteria.
This study provides a robust framework for future SSc classification development.