2004
DOI: 10.12968/jowc.2004.13.1.26564
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Reliability and validity of DESIGN, a tool that classifies pressure ulcer severity and monitors healing

Abstract: Based on our results, DESIGN was found to have both high inter-rater reliability and high validity among the seven nurses who quantitatively evaluated the wound-healing progress of the pressure ulcers in this study.

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Cited by 85 publications
(99 citation statements)
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“…17,23 Several classification tools have also been addressed and supervised tools have been shown to surpass the others. 24,25 In many studies, 2,26,27 assessment of the tissue type is not achieved ͑only the grade or the surface of the wound is provided͒ or is only partially accomplished.…”
Section: Visual Wound Assessmentmentioning
confidence: 99%
“…17,23 Several classification tools have also been addressed and supervised tools have been shown to surpass the others. 24,25 In many studies, 2,26,27 assessment of the tissue type is not achieved ͑only the grade or the surface of the wound is provided͒ or is only partially accomplished.…”
Section: Visual Wound Assessmentmentioning
confidence: 99%
“…4,22 The tool includes assessment of the depth, exudate, size, infection/ inflammation, granulation tissue, necrotic tissue, and pocketing. The total score is calculated from six items, excluding the depth, and ranges from 0 to 66 points, with higher scores representing more severe PUs.…”
Section: Methods Designmentioning
confidence: 99%
“…Of note, it has been reported that both wound size and pocketing were high intra-class correlation coefficient (r=1). 4 Thus, we evaluated both wound size and pocketing obtained from the clinical notes by WOCNs.…”
Section: Data Collectionmentioning
confidence: 99%
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“…However, none of them has dealt with the problem of automated lesion classification. For example [4] proposes a tool using images for classifying the pressure ulcer and for healing monitoring but the related data are entered manually and the classification is done manually as well. In [5] the lesions are measured from photos using a 3D acquisition system employing a pattern-projecting device, so that the spatial data can be extracted, but the automated lesion classification problem is not handled.…”
mentioning
confidence: 99%