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Infant CD4 C868T polymorphism is associated with increased human immunodeficiency virus (HIV-1) acquisition

2010· article· en· W1528465522 on OpenAlexafffund
Robert Y. Choi, Carey Farquhar, Jennifer A. Juno, Dorothy Mbori‐Ngacha, Barbara Lohman‐Payne, Françoise C. M. Vouriot, Stephen Wayne, J. Tuff, Rose Bosire, Grace John‐Stewart, Keith R. Fowke

Bibliographic record

VenueClinical & Experimental Immunology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentFogarty International CenterNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsImmunologySingle-nucleotide polymorphismSNPCohortHazard ratioViral loadConfidence intervalAlleleHuman immunodeficiency virus (HIV)MedicineBiologyVirusVirologySidaViral diseaseInternal medicineGenotypeGeneticsGene

Abstract

fetched live from OpenAlex

The C868T single nucleotide polymorphism (SNP) in the CD4 receptor encodes an amino acid change that could alter its structure and influence human immunodeficiency virus (HIV-1) infection risk. HIV-1-infected pregnant women in Nairobi were followed with their infants for 1 year postpartum. Among 131 infants, those with the 868T allele were more likely than wild-type infants to acquire HIV-1 overall [hazard ratio (HR) = 1.92, 95% confidence interval (CI) 1.05, 3.50, P = 0.03; adjusted HR = 2.03, 95% CI 1.03, 3.98, P = 0.04], after adjusting for maternal viral load. This SNP (an allele frequency of approximately 15% in our cohort) was associated with increased susceptibility to mother-to-child HIV-1 transmission, consistent with a previous study on this polymorphism among Nairobi sex workers.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.343
Teacher spread0.319 · 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

Citations37
Published2010
Admission routes2
Has abstractyes

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