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Record W2985149000 · doi:10.1111/hiv.12825

Post‐exposure prophylaxis following consented sexual exposure: impact of national recommendations on user profile, drug regimens and estimates of averted HIV infections

2019· article· en· W2985149000 on OpenAlexaff
IO Pereira, A Pati Pascom, Gláucio Mosimann, Filipe Barros Perini, RA Coelho, Fernanda Rick, Adèle Schwartz Benzaken, Vivian Iida Avelino‐Silva

Bibliographic record

VenueHIV Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicinePre-exposure prophylaxisPost-exposure prophylaxisContext (archaeology)Human immunodeficiency virus (HIV)Christian ministryOutreachMedical prescriptionReproductive healthMen who have sex with menEnvironmental healthFamily medicinePopulationPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of the study was to describe the characteristics, impact and outreach of post-exposure prophylaxis (PEP) for sexual exposure in Brazil. METHODS: We used secondary data from the Brazilian Ministry of Health to describe the impact of national guidelines on the frequency of prescription, user profile and antiretroviral regimens. We also estimated the number of potentially averted HIV infections attributable to PEP for consented sexual exposure between 2009 and 2017. RESULTS: A total of 260 457 PEP regimens were prescribed to individuals ≥ 14 years old; 104 613 (40.2%) were prescribed for consented sexual exposure, with an increasing frequency since 2011. Drugs used in PEP regimens underwent significant modifications during the period, reflecting national recommendations. We estimated that there were up to 3138 potentially averted HIV infections attributable to PEP for consented sexual exposure between 2009 and 2017. CONCLUSIONS: In the context of a combined HIV prevention strategy, PEP is still an essential tool for individuals for whom other methods are contraindicated or fail to be applied.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.340
Teacher spread0.322 · 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 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

Citations10
Published2019
Admission routes1
Has abstractyes

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