Linked Life Data

9820278

Source:http://linkedlifedata.com/resource/pubmed/id/9820278

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
1999-4-7
pubmed:abstractText
The capability to reliably predict long-term in vivo wear of polyethylene would be of great value for the early identification of problematic total hip designs. Formal quantitative estimates of long-term polyethylene wear were made from a series of 197 patients who had a total hip arthroplasty and who were followed for a minimum of 10 years; the estimates were based on the wear that was apparent radiographically at nominally 2 years after the operation. A newly developed digital image-analysis edge-detection procedure was applied to 1,237 archived follow-up radiographs. The edge-detection measurements were analyzed with a robust regression random-coefficients statistical formulation developed especially to address the distributions of wear rate observed across this population over time. Formal regression equations were reported, which can be used to estimate late-wear depth for a patient radiographed at a 2-year follow-up visit. Series wide, the correlation between predicted and observed wear depths was 0.73 at 4 years, with a correlation decline of approximately 0.03 per additional year.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0736-0266
pubmed:author
pubmed:issnType
Print
pubmed:volume
16
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
557-63
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Prediction of long-term polyethylene wear in total hip arthroplasty, based on early wear measurements made using digital image analysis.
pubmed:affiliation
Department of Orthopaedic Surgery, University of Iowa, Iowa City 52242, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't