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Record W2060276130 · doi:10.1155/2008/840948

Infection with Hepatitis C Virus among HIV-Infected Pregnant Women in Thailand

2008· article· en· W2060276130 on OpenAlexaff
Denise J. Jamieson, Natapakwa Skunodom, Thanyanan Chaowanachan, Anuvat Roongpisuthipong, William A. Bower, Tawee Chotpitayasunondh, Wendy Bhanich Supapol, Wendi L. Kuhnert, Wimol Siriwasin, Jeffrey Wiener, Sanay Chearskul, Michelle S. McConnell, Nathan Shaffer

Bibliographic record

VenueInfectious Diseases in Obstetrics and Gynecology · 2008
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of Toronto
FundersCenters for Disease Control and Prevention
KeywordsCoinfectionMedicineSeroprevalenceHepatitis C virusTransmission (telecommunications)CohortEpidemiologyHepatitis CVirologyPregnancyCohort studyHuman immunodeficiency virus (HIV)ObstetricsImmunologyVirusInternal medicineSerologyAntibodyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to describe the epidemiology of coinfection with hepatitis C virus (HCV) and HIV among a cohort of pregnant Thai women. METHODS: Samples from 1771 pregnant women enrolled in three vertical transmission of HIV studies in Bangkok, Thailand, were tested for HCV. RESULTS: Among HIV-infected pregnant women, HCV seroprevelance was 3.8% and the active HCV infection rate was 3.0%. Among HIV-uninfected pregnant women, 0.3% were HCV-infected. Intravenous drug use by the woman was the factor most strongly associated with HCV seropositivity. Among 48 infants tested for HCV who were born to HIV/HCV coinfected women, two infants were HCV infected for an HCV transmission rate of 4.2% (95% 0.51-14.25%). CONCLUSIONS: HCV seroprevalence and perinatal transmission rates were low among this Thai cohort of HIV-infected pregnant women.

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)0.001
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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Same venueInfectious Diseases in Obstetrics and GynecologySame topicHepatitis C virus researchFrench-language works237,207