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HIV drug-resistance testing on archived samples to help current clinical decisions

2000· letter· en· W2052119440 on OpenAlexaboutno aff
Gillian Dean, Martin Fisher, Clive Loveday

Bibliographic record

VenueAIDS · 2000
Typeletter
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsRegimenMedicineDrug resistanceZidovudineRitonavirAbacavirHIV drug resistanceViral loadIntensive care medicineInternal medicineImmunologyHuman immunodeficiency virus (HIV)Viral diseaseBiologyAntiretroviral therapyGenetics

Abstract

fetched live from OpenAlex

Recent studies have shown that the use of genotypic-resistance testing to assist therapeutic decision making has a significant benefit on virological response in pre-treated individuals switching to an alternative regimen [1]. In their editorial review, Rodriguez-Rosado et al. [2] list several clinical situations for which HIV drug-resistance testing may be useful, but state that in pre- treated patients without evidence of failure, drug-resistance profiling is currently not justified. In the past 10 months we have performed resistance testing using automated sequencing (TruGene HIV-1 genotyping assay; Visible Genetics Inc., Toronto, Canada) on archived samples for five patients, who at the time of the request had undetectable viral loads. These samples were taken and stored during previous periods of drug failure. On each occasion, although there was no evidence of failure on the current regimen, the development of severe drug side-effects (peripheral neuropathy in three cases) or the non-tolerance of one or more of the antiviral agents (ritonavir in two cases) led to a search for an alternative regimen. As these patients had taken at least three preceding antiviral combinations (median 4, range 3–7), therapeutic choices were limited. Table 1 summarizes previous antiretroviral regimens, genotypic-resistance test results, and clinical outcome for patients A–E. For patient A with peripheral neuropathy, the lack of zidovudine (ZDV) resistance allowed the re-cycling of this drug and the cessation of stavudine (d4T). The resistance assay for patient B suggested, in the absence of nucleoside resistance, that it may be safe to discontinue protease inhibitors (PI) as these drugs were unlikely to contribute significantly to virological suppression. Patient C had previously been intolerant of PI, but had not had virological rebound while taking this class of drug. The resistance assay in this case precluded the re-use of ZDV and therefore, on developing severe peripheral neuropathy, d4T was changed to amprenavir. In patient D, again after developing peripheral neuropathy, d4T was substituted by ZDV and lamivudine (3TC). Despite the previous use of ZDV, this combination was introduced to exploit the favourable effect that 3TC may have on the suppression of ZDV resistance, with the possibility of ZDV resensitization [3]. In addition, it was thought that 3TC might increase the fidelity of the HIV reverse transcriptase [4] and limit further transcription errors. Finally in patient E, ritonavir and indinavir were changed to amprenavir in the absence of specific mutations to the newer PI. In all these individuals retrospective genotypic-resistance testing, at times −34, −5, −24, −7 and −6 months, respectively, allowed a more informed choice for the next antiviral combination. Each patient has since reported an improvement of the original clinical problem, while maintaining virological suppression.Table 1: Previous antiretroviral regimens, genotypic-resistance test results, and clinical outcome for patients A–E. We therefore propose that retrospective resistance testing may play an important role in pre-treated patients without evidence of failure. Clinicians should be encouraged to store specimens at regular intervals, especially at the time of treatment failure, even if resistance testing at that specific time-point would not alter management. Given that long-term toxicity in this patient group is likely to increase over time, resistance testing on archived specimens may be useful at future time-points to ensure continued viral suppression and clinical benefit. Gillian L. Deana Martin Fishera Clive Lovedayb

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.007

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 head0.109
GPT teacher head0.361
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2000
Admission routes1
Has abstractyes

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