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Record W2128100284 · doi:10.1086/320192

Patterns of Plasma Human Immunodeficiency Virus Type 1 RNA Response to Antiretroviral Therapy

2001· article· en· W2128100284 on OpenAlexaff
Weijie Huang, Victor De Gruttola, Margaret A. Fischl, S. Hammer, Douglas D. Richman, Diane V. Havlir, Roy M. Gulick, K. Squires, John W. Mellors

Bibliographic record

VenueThe Journal of Infectious Diseases · 2001
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsColumbia College
FundersNational Institute of Allergy and Infectious Diseases
KeywordsAntiretroviral therapyMedicineHuman immunodeficiency virus (HIV)Viral loadImmunologyLentivirusSidaFast trackClinical trialInternal medicineVirologyIntensive care medicineViral diseaseSurgery

Abstract

fetched live from OpenAlex

Early identification of treatment failure among human immunodeficiency virus (HIV) type 1--infected patients receiving antiretroviral therapy could enable clinicians to modify inadequate regimens and to improve treatment response. Clinical definitions of treatment failure, however, may not be ideally suited for this purpose. This study empirically characterizes the patterns of HIV-1 RNA response to antiretroviral therapy in patients in 4 AIDS clinical trials. The approach assumed 2 patterns of HIV-1 response: "on track," for eventual suppression to HIV-1 RNA levels below the limit of quantification, and "off track," for deviation from this response. The results of this on- or off-track classification generally agreed with the protocol-defined outcomes of virologic success and failure, thus validating these commonly used definitions. Overall, only a minority of patients went off track because of suboptimal HIV-1 RNA response by the first follow-up visit. Most patients who went off track did so at later time points and had sharp unexpected rebounds without prior indication of a suboptimal response.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.290
Teacher spread0.274 · 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 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

Citations46
Published2001
Admission routes1
Has abstractyes

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