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Record W2162900142

Human immunodeficiency virus co-infection increases placental parasite density and transplacental malaria transmission in Western Kenya.

2009· article· en· W2162900142 on OpenAlexaff
Steven D. Perrault, Jan Hájek, Kathleen Zhong, Simon O. Owino, Moses Sichangi, Geoffrey Smith, Ya Ping Shi, Julie M. Moore, Kevin C. Kain

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMalariaPlasmodium falciparumContext (archaeology)Odds ratioPlacentaImmunologyPregnancyBiologyTransmission (telecommunications)MedicineVirologyCord bloodParasite hostingObstetricsFetusInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Plasmodium falciparum malaria and human immunodeficiency virus (HIV)-1 adversely interact in the context of pregnancy, however little is known regarding the influence of co-infection on the risk of congenital malaria. We aimed to determine the prevalence of placental and congenital malaria and impact of HIV co-infection on trans-placental malaria transmission in 157 parturient women and their infants by microscopy and by quantitative real-time polymerase chain reaction (PCR) in western Kenya. The prevalence of placental and cord blood infections were 17.2% and 0% by microscopy, and 33.1% and 10.8% by PCR. HIV co-infection was associated with a significant increase in placental parasite density (P < 0.05). Cord blood malaria prevalence was increased in co-infected women (odds ratio [OR] = 5.42; 95% confidence interval [CI] = 1.90-15.47) and correlated with placental parasite density (OR = 2.57; 95% CI = 1.80-3.67). A 1-log increase in placental monocyte count was associated with increased risk of congenital infection (P = 0.001) (OR = 48.15; 95% CI = 4.59-505.50). The HIV co-infected women have a significantly increased burden of placental malaria that increases the risk of congenital infection.

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 score0.150
Threshold uncertainty score0.613

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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

Citations73
Published2009
Admission routes1
Has abstractyes

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