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14505770
Source:
http://linkedlifedata.com/resource/pubmed/id/14505770
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pubmed-article:14505770
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lifeskim:mentions
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umls-concept:C0443131
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pubmed-article:14505770
pubmed:issue
9
lld:pubmed
pubmed-article:14505770
pubmed:dateCreated
2003-9-24
lld:pubmed
pubmed-article:14505770
pubmed:abstractText
We examined whether a breast cancer risk prediction model other than the Gail et al. model performs better at discriminating between women who will and who will not develop the disease.
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eng
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pubmed-article:14505770
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pubmed-article:14505770
pubmed:citationSubset
IM
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pubmed-article:14505770
pubmed:status
MEDLINE
lld:pubmed
pubmed-article:14505770
pubmed:month
Sep
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pubmed-article:14505770
pubmed:issn
0895-4356
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pubmed-article:14505770
pubmed:author
pubmed-author:RosnerBernard...
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pubmed-author:LouieMary MMM
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pubmed-article:14505770
pubmed:issnType
Print
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pubmed-article:14505770
pubmed:volume
56
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pubmed-article:14505770
pubmed:owner
NLM
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pubmed-article:14505770
pubmed:authorsComplete
Y
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pubmed-article:14505770
pubmed:pagination
856-61
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pubmed-article:14505770
pubmed:dateRevised
2007-11-14
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pubmed-article:14505770
pubmed:year
2003
lld:pubmed
pubmed-article:14505770
pubmed:articleTitle
Breast cancer risk prediction with a log-incidence model: evaluation of accuracy.
lld:pubmed
pubmed-article:14505770
pubmed:affiliation
Department of Epidemiology, CB #7435, University of North Carolina, Chapel Hill, NC 27599-7435, USA. beverly_rockhill@unc.edu
lld:pubmed
pubmed-article:14505770
pubmed:publicationType
Journal Article
lld:pubmed
pubmed-article:14505770
pubmed:publicationType
Research Support, U.S. Gov't, P.H.S.
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