132,515 research outputs found
Mucositis
No full textNoor Al-Dasooqi, Dorothy M. Keefe and Stephen T. Sonishttp://trove.nla.gov.au/version/19402933
paddy keefe
No full textpaddy keefe a, avShane O'Dea suggests this is related to P D Q, (pretty damn quick).I don't follow this WKUsed I and SupUsed I and Sup1Not use
Supportive care in cancer: developments in treatment and symptom management
No full textDorothy Keefe and Emma H. Batemanhttp://ci.nii.ac.jp/naid/4001901314
Gastrointestinal toxicity of targeted anti-cancer therapy
No full textDorothy Keefe and Emma Batemanhttp://www.treatment-strategies.co.uk
Alien Registration- Keefe, James D. (Lebanon, York County)
No full texthttps://digitalmaine.com/alien_docs/3494/thumbnail.jp
Geriatric oncology: A medical sub-specialty whose time has come
No full textDorothy Keefe and Robert Prows
Phase II study of epirubicin, cisplatin and continuous infusion 5-fluorouracil (ECF) for carcinoma of unknown primary site
No full textF. X. Parnis, I. N. Olver, D. Kotasek, J. Norman, A. Taylor, J. Russell, K. Patterson, D. Keefe & T. Marafiot
The combination of oral and small intestinal mucositis, pediatrics and biomarkers - A particularly tricky problem!
No full textDorothy M. Keefe and Rachel J. Gibso
Sucrose breath testing and intestinal mucositis
No full textCommentary to: A Non-Invasive Method for Detection of Intestinal Mucositis Induced by Different Classes of Chemotherapy Drugs in the Rat Gordon S. Howarth, Katie L Tooley, Geoffrey P. Davidson and Ross N. ButlerDorothy M. Keefe and Rachel J. Gibso
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
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