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Record W1968922678 · doi:10.1258/095646206780071108

Association between HSV-2 and HIV-1 viral load in semen, cervico-vaginal secretions and genital ulcers of Thai men and women

2006· article· en· W1968922678 on OpenAlexaff
Kathryn Chu, Sukhum Jiamton, Frances M. Cowan, Bussakorn Mahakkanukrauh, Ruengpung Suttent, Noah Jamie Robinson, Sylvie Deslandes, Éric Frost, Pongsakdi Chaisilwattana, Puan Suthipinittharm, Heiner Grosskurth, David J. Brown, Shabbar Jaffar

Bibliographic record

VenueInternational Journal of STD & AIDS · 2006
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineSex organViral loadSemenHerpes simplex virusViral sheddingSexually transmitted diseaseHerpes GenitalisVaginaGenital ulcerViral diseaseConfidence intervalHuman immunodeficiency virus (HIV)VirusGynecologyImmunologyInternal medicineGenital herpesSurgeryAndrologySyphilisBiology

Abstract

fetched live from OpenAlex

We studied the association between herpes simplex virus type-2 (HSV-2) and HIV-1 viralload in plasma, semen, cervico-vaginal secretions and genital ulcers. Forty-seven (68%) men and 57 (80%) women were HSV-2 antibody positive, of whom 12 (26%, 95% confidence interval [CI] 20, 32) and five (8%, 95% CI 4, 12), respectively, had HSV-2 genital shedding detected by polymerase chain reaction. The mean HIV-1 seminal and cervico-vaginal viral loads did not differ significantly according to the presence of HSV-2 shedding. Eleven men and 15 women presented with genital ulcers; all ulcers were due to HSV-2. Ten men and nine women were followed up over six days: the mean (95% CI) HIV-1 log viral load copies/mL in the genital ulcers at baseline and final visits were 2.5 (2.3, 2.7) and 3.1 (2.0, 4.2) for men and 3.0 (2.6, 3.4) and 2.7 (2.3, 3.1) for women. These findings do not support the hypothesis that HSV-2 increases the HIV-1 viral load in genital secretions.

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 score0.011
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.260
Teacher spread0.255 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of STD & AIDSSame topicHerpesvirus Infections and TreatmentsFrench-language works237,207