MétaCan
Menu
← Back to cohort
Record W3176883758 · doi:10.1093/cid/ciab587

Immunological Correlates of the HIV-1 Replication-Competent Reservoir Size

2021· article· en· W3176883758 on OpenAlexaff
Sherazaan D. Ismail, Catherine Riou, Sarah Joseph, Nancie M. Archin, David M. Margolis, Alan S. Perelson, Tyler Cassidy, Melissa-Rose Abrahams, Matthew Moeser, Lyle R. McKinnon, Farzana Osman, Quarraisha Abdool Karim, Salim S. Abdool Karim, Ronald Swanstrom, Carolyn Williamson, Nigel Garrett, Wendy A. Burgers

Bibliographic record

VenueClinical Infectious Diseases · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Manitoba
FundersNational Institutes of HealthUniversity of Cape TownDepartment of Science and Technology, Republic of South AfricaSouth African Medical Research CouncilWellcome TrustUniversity of North CarolinaNational Institute of Allergy and Infectious DiseasesPoliomyelitis Research FoundationBill and Melinda Gates FoundationFogarty International CenterUnited States Agency for International Development
KeywordsMedicineHuman immunodeficiency virus (HIV)Antiretroviral therapyCD8ImmunologyVirologyViral replicationNadirImmunopathologyCD4-CD8 RatioVirusViral loadSidaViral diseaseImmune systemLymphocyte subsets

Abstract

fetched live from OpenAlex

Understanding what shapes the latent human immunodeficiency virus type 1 (HIV-1) reservoir is critical for developing strategies for cure. We measured frequency of persistent HIV-1 infection after 5 years of suppressive antiretroviral therapy initiated during chronic infection. Pretreatment CD8+ T-cell activation, nadir CD4 count, and CD4:CD8 ratio predicted reservoir size.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.032
GPT teacher head0.335
Teacher spread0.302 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueClinical Infectious Diseases→Same topicHIV Research and Treatment→French-language works237,207→