MétaCan
Menu
Back to cohort
Record W2022687162 · doi:10.1097/qad.0b013e328359a95c

Tenofovir use and pregnancy among women initiating HAART

2012· article· en· W2022687162 on OpenAlexaff
Mhairi Maskew, Daniel Westreich, Cindy Firnhaber, Ian Sanne

Bibliographic record

VenueAIDS · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsInstitute of Health Economics
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentU.S. Public Health Service
KeywordsTenofovirPregnancyObstetricsMedicineSidaHuman immunodeficiency virus (HIV)GynecologyViral diseaseVirologyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Recent studies have raised concerns about a change in rates of pregnancy among HIV-negative women exposed to tenofovir. Here, our objective was to determine among HIV-positive women whether use of tenofovir at HAART initiation or thereafter is associated with subsequent changes in incidence of pregnancy. DESIGN: Analysis of prospectively collected clinical data. METHODS: We used Cox proportional hazards models and logistic regression to estimate hazard ratios and odds ratios for the association of baseline tenofovir use and first incident pregnancy. We used marginal structural Cox models to estimate hazard ratios for the association of current tenofovir use and time to first incident pregnancy. RESULTS: We studied 7275 women, of whom 1199 were initiated on tenofovir-based HAART regimens, and who experienced a total of 894 pregnancies in 17,200 person-years of follow-up. Analyses showed slight reductions in hazards of pregnancy among women who used tenofovir but without sufficient precision to draw strong conclusions. Sensitivity analyses confirmed main results. CONCLUSIONS: Tenofovir may be associated with a lower hazard or rate of pregnancy in women receiving HAART. However, conclusions are limited by low precision, the observational nature of the data, and possible uncontrolled confounding by temporal trends in contraception use and other factors.

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 score0.015
Threshold uncertainty score0.350

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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAIDSSame topicHIV/AIDS drug development and treatmentFrench-language works237,207