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Record W1981659119 · doi:10.2217/17455057.5.1.55

Rapid Testing at Labor and Delivery to Prevent Mother-To-Child Hiv Transmission in Developing Settings: Issues and Challenges

2009· review· en· W1981659119 on OpenAlexaff
Nitika Pant Pai, Marina B. Klein

Bibliographic record

VenueWomen s Health · 2009
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsPsychological interventionMedicineTransmission (telecommunications)PregnancyHuman immunodeficiency virus (HIV)Developing countryIntensive care medicineFamily medicineEnvironmental healthNursingEconomic growth

Abstract

fetched live from OpenAlex

Worldwide, approximately 2.5 million children (95% CI: 2.2-2.6) are living with HIV infection. In 2007 alone, approximately 420,000 children (95%CI: 350,000-540,000) were newly infected with HIV - a vast majority of these infections were acquired through maternal-fetal transmission. Many of these infections could have been reduced by timely diagnosis and the delivery of interventions aimed at preventing mother-to-child HIV transmission. This perspective examines the attitudes preventing women from accessing HIV testing early on during pregnancy and the issues and challenges that remain in the institutionalization of interventions to prevent mother-to-child HIV transmission at labor and delivery. Socio-cultural and economic factors prevent women from accessing testing at an opportune time during pregnancy. In addition, a lack of adequate infrastructure often prevents timely delivery of interventions to those who access testing at the last minute (i.e., during labor and delivery). In the wake of a pediatric HIV epidemic and the need for lifelong provision of antiretroviral therapy to infected children, a simple strategy for provision of round-the-clock rapid testing and counseling services in the labor rooms may be cost saving to the healthcare systems worldwide.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.083
GPT teacher head0.392
Teacher spread0.309 · 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.

Study designOther design
Domainnot available
GenreReview

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
Published2009
Admission routes1
Has abstractyes

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