Literature DB >> 17077744

Pain drawing scoring is not improved by inclusion of patient-reported pain sensation.

Neal W Sanders1, N Horace Mann, Dan M Spengler.   

Abstract

STUDY
DESIGN: This is a retrospective study of 250 patients who describe low back pain with pain drawings. A computer application using artificial neural networks was designed to analyze pain drawings and evaluate the contribution of pain sensation to drawing classification.
OBJECTIVE: The primary goal of this study was to assess the contribution of patient recorded pain sensation marks in classifying pain drawings into one of five broadly defined categories. The hypothesis was that including pain sensation would improve classification. SUMMARY OF BACKGROUND DATA: With no perfect diagnostic test for patients with low back pain, many approaches have been proposed and are used. One common diagnostic tool is the pain drawing. Several quantitative methods have been proposed to score the drawings. Some methods use pain sensation in the scoring; however, the contribution of pain sensation has not been defined.
METHODS: A custom computer application classified the pain drawing. Data consisted of 250 pain drawings from patients with low back pain.
RESULTS: Patient recorded pain sensation is not necessary in computer-based scoring of pain drawings.
CONCLUSION: Patient-reported pain sensation does not improve classification when quantitatively scoring pain drawings.

Entities:  

Mesh:

Year:  2006        PMID: 17077744     DOI: 10.1097/01.brs.0000244674.99258.f9

Source DB:  PubMed          Journal:  Spine (Phila Pa 1976)        ISSN: 0362-2436            Impact factor:   3.468


  5 in total

1.  Psychometric Study of the Pain Drawing.

Authors:  Lisa H Trahan; Emily Cox-Martin; Carrie E Johnson; Patrick M Dougherty; Jun Yu; Lei Feng; Christina Cook; Diane M Novy
Journal:  J Appl Biobehav Res       Date:  2017-04-07

2.  Computer-aided surface estimation of pain drawings - intra- and inter-rater reliability.

Authors:  Ann L Persson; Sofia Garametsos; Jonna Pedersen
Journal:  J Pain Res       Date:  2011-05-15       Impact factor: 3.133

3.  Profiling the Location and Extent of Musicians' Pain Using Digital Pain Drawings.

Authors:  Cinzia Cruder; Deborah Falla; Francesca Mangili; Laura Azzimonti; Liliana S Araújo; Aaron Williamon; Marco Barbero
Journal:  Pain Pract       Date:  2017-05-28       Impact factor: 3.183

4.  Computerized assessment of pain drawing area: A pilot study.

Authors:  Anna Wenngren; Britt-Marie Stålnacke
Journal:  Neuropsychiatr Dis Treat       Date:  2009-08-20       Impact factor: 2.570

5.  Attitudes of patients toward adoption of 3D technology in pain assessment: qualitative perspective.

Authors:  Fotios Spyridonis; Gheorghita Ghinea; Andrew O Frank
Journal:  J Med Internet Res       Date:  2013-04-10       Impact factor: 5.428

  5 in total

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