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Record W3092090615 · doi:10.1097/qad.0000000000002707

Differential effects of antiretroviral drug toxicity in male versus female children who are HIV-exposed but uninfected

2020· article· en· W3092090615 on OpenAlexafffund
Julian C. Gilmore, Lena Serghides, Reina Bendayan

Bibliographic record

VenueAIDS · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPregnancyMedicineIn uteroFetusExacerbationToxicityDevelopmental toxicityImmunologyDrugPhysiologyBiologyInternal medicinePharmacology

Abstract

fetched live from OpenAlex

: In recent years, widespread use of antiretroviral therapy (ART) during pregnancy has been increasingly effective in reducing risk of vertical transmission of HIV, with over 80% of pregnant women living with HIV now accessing ART, and a 41% reduction in new infections in children between 2010 and 2018. Despite these strides, the developmental toxicity of widely administered antiretroviral drugs (ARVs) remains poorly described and existing literature often fails to account for fetal and infant sex as a variable. Recent reports have identified associations between in-utero exposure to commonly used antiretroviral regimens and alteration in neurodevelopment, growth, and metabolism amongst children who are HIV-exposed but uninfected, with findings of sex differences in the prevalence and severity of ARV toxicity. These differences are potentially explained by variable exposure to ARV drugs in utero or exacerbation of existing sex-linked risk factors. Fetal ARV exposure is mediated by placental and fetal drug transporters and metabolic enzymes, which may contribute to the manifestation of sex differences. Existing evidence of sex differences in ARV toxicity in fetal development is concerning, and demands further research to guide optimal treatment options for maternal health and prevention of vertical HIV transmission.

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)0.001
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 score0.168
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

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