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Record W2026956966 · doi:10.1517/17530050802708981

Trofile HIV co-receptor usage assay

2009· article· en· W2026956966 on OpenAlexaff
Andrew Low, Rachel A. McGovern, P. Richard Harrigan

Bibliographic record

VenueExpert Opinion on Medical Diagnostics · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTropismCCR5 receptor antagonistMaravirocCo-receptorHuman immunodeficiency virus (HIV)MedicineComputational biologyTissue tropismPharmacologyCXCR4VirologyBioinformaticsReceptorBiologyChemokine receptorVirusInternal medicineChemokine

Abstract

fetched live from OpenAlex

BACKGROUND: The introduction of CCR5 antagonists increases the options available for constructing therapeutic drug regimens for HIV-positive patients. However, as these drugs do not inhibit HIV variants that use the CXCR4 co-receptor, a pretreatment test is required to determine accurately HIV co-receptor usage (tropism) before initiating CCR5 antagonist-based therapy. OBJECTIVE/METHOD: To discuss the Monogram Trofile assay as a diagnostic tool for determining HIV tropism by critically reviewing reported literature and available data. CONCLUSIONS: Monogram Trofile has become, largely by default, the de facto standard for HIV tropism assay. However, there is significant room for improvement in the speed, cost and availability of the test. Furthermore, the test is not quantitative, requires high-input HIV RNA viral loads, and produces results that are less biologically stable than expected. These technical considerations may limit the use of CCR5 antagonists in therapy. Nevertheless, this test is likely to remain the most widely used tropism diagnostic for the short term. We expect that a more practical and possibly more accurate method for measuring HIV tropism can be developed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.045

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.337
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2009
Admission routes1
Has abstractyes

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