- Abstract:
-
Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping. Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa. Methods: Cases from Phidisa of treatment change following failure were identified that ... Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's Version
- Files:
-
-
(pdf, 1.8MB)
-
- Publisher copy:
- 10.4102/sajhivmed.v17i1.450
-
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- Publisher:
- Health and Medical Publishing Group Publisher's website
- Journal:
- Southern African Journal of HIV Medicine Journal website
- Volume:
- 17
- Issue:
- 1
- Pages:
- a450
- Publication date:
- 2016-06-30
- Acceptance date:
- 2016-04-22
- DOI:
- EISSN:
-
2078-6751
- ISSN:
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1608-9693
- Pubs id:
-
pubs:897615
- URN:
-
uri:93aeb64d-1941-4428-99b1-45055cd3602c
- UUID:
-
uuid:93aeb64d-1941-4428-99b1-45055cd3602c
- Local pid:
- pubs:897615
- Language:
- English
- Copyright holder:
- Revell et al.
- Copyright date:
- 2016
- Notes:
- © 2016. The Authors. Licensee: AOSIS. This work is licensed under the Creative Commons Attribution License.
Journal article
Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa
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National Institute of Allergy
and Infectious Diseases
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