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Record W2418527354 · doi:10.1177/135965350100600105

Predictors of Virological Response in HIV-Infected Patients to Salvage Antiretroviral Therapy that Includes Nelfinavir

2001· article· en· W2418527354 on OpenAlexaff
Sharon Walmsley, Mark Becker, Min Zhang, Atul Humar, P. Richard Harrigan

Bibliographic record

VenueAntiviral Therapy · 2001
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsAIDS VancouverUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsNelfinavirSalvage therapyMedicineReverse transcriptaseInternal medicineProtease inhibitor (pharmacology)Reverse-transcriptase inhibitorViral loadVirologyIndinavirZidovudineSidaPopulationLentivirusCohortHuman immunodeficiency virus (HIV)GastroenterologyBiologyViral diseaseAntiretroviral therapyChemotherapyRNA

Abstract

fetched live from OpenAlex

Different salvage strategies have been used to regain control in patients with HIV who have virological failure on combination antiretroviral therapy. We conducted a cohort study of 63 extensively antiretroviral pretreated patients who initiated nelfinavir as part of salvage therapy, to determine predictors of virological response. The maximum HIV RNA response was >0.5 log10 copies/ml reduction in 43 patients (68%), including 21 patients (33%) who had suppression to <500 copies/ml. Corresponding response rates at 24 weeks were 41 and 19%, respectively. Responders and non-responders could not be distinguished by mean baseline HIV RNA or CD4 cell count, duration of prior protease inhibitor (PI) use, introduction of an initial non-nucleoside reverse transcriptase inhibitor or the number of antiretroviral agents changed when nelfinavir was added, likely reflecting the homogeneity of the population studied. The only parameter predictive of response was virus genotype. Response rates were lower in patients with increasing numbers of primary (P=0.045) or secondary (P=0.001) PI mutations. The addition of increasing numbers of reverse transcriptase mutations further impaired response rates (P=0.004).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.283
Teacher spread0.258 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2001
Admission routes1
Has abstractyes

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