Multivariate prediction of upper limb prosthesis acceptance or rejection.
Objective. To develop a model for prediction of upper limb prosthesis use or rejection. Design. A questionnaire exploring factors in prosthesis acceptance was distributed internationally to individuals with upper limb absence through community-based support groups and rehabilitation hospitals. Subje...
| Publicado en: | Disability & Rehabilitation: Assistive Technology Vol. 3; no. 4; pp. 181 - 193 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105704792&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105704792 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17483107 1X04 jtl: Disability & Rehabilitation: Assistive Technology issn: 17483107 maglogo: Y pubinfo: dt: Jul2008 vid: 3 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 105704792 105704792 2010057277 10.1080/17483100701869826 NLM19238719 105704792 ppf: 181 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Multivariate prediction of upper limb prosthesis acceptance or rejection. aug: au: Biddiss EA Chau TT affil: Bloorview Research Institute, Toronto, The Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada sug: subj: Forecasting Limb Prosthesis Psychosocial Factors Patient Compliance Upper Extremity Adolescence Adult Age Factors Aged Aged, 80 and Over Algorithms Chi Square Test Child Child, Preschool Comparative Studies Data Analysis Software Decision Trees Descriptive Statistics Female Funding Source Goodness of Fit Chi Square Test Hospitals, Community Infant Logistic Regression Male Mann-Whitney U Test Middle Age Models, Theoretical Multivariate Analysis Ontario Patient Attitudes Evaluation Prosthetic Fitting Questionnaires Reliability and Validity Sensitivity and Specificity Spearman's Rank Correlation Coefficient Step-Wise Multiple Regression Support Groups T-Tests Time Factors Univariate Statistics Human Adolescent: 13-18 years Adult: 19-44 years Aged: 65+ years Aged, 80 & over Child: 6-12 years Child, Preschool: 2-5 years Infant: 1-23 months Middle Aged: 45-64 years Female Male ab: Objective. To develop a model for prediction of upper limb prosthesis use or rejection. Design. A questionnaire exploring factors in prosthesis acceptance was distributed internationally to individuals with upper limb absence through community-based support groups and rehabilitation hospitals. Subjects. A total of 191 participants (59 prosthesis rejecters and 132 prosthesis wearers) were included in this study. Methods. A logistic regression model, a C5.0 decision tree, and a radial basis function neural network were developed and compared in terms of sensitivity (prediction of prosthesis rejecters), specificity (prediction of prosthesis wearers), and overall cross-validation accuracy. Results. The logistic regression and neural network provided comparable overall accuracies of approximately 84 +/- 3%, specificity of 93%, and sensitivity of 61%. Fitting time-frame emerged as the predominant predictor. Individuals fitted within two years of birth (congenital) or six months of amputation (acquired) were 16 times more likely to continue prosthesis use. Conclusions. To increase rates of prosthesis acceptance, clinical directives should focus on timely, client-centred fitting strategies and the development of improved prostheses and healthcare for individuals with high-level or bilateral limb absence. Multivariate analyses are useful in determining the relative importance of the many factors involved in prosthesis acceptance and rejection. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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