MétaCan
Menu
Back to cohort

Postexposure prophylaxis (PEP) of HIV among adults

2023· review· en· W4366995481 on OpenAlexaff
Mohamed Toufic El Hussein, Ivan Viktorovich Malyshev

Bibliographic record

VenueThe Nurse Practitioner · 2023
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsRockyview General HospitalMount Royal University
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)MedicineAntiretroviral therapyIntensive care medicineTransmission (telecommunications)Clinical PracticeANTIRETROVIRAL AGENTSPost-exposure prophylaxisImmunologyViral loadFamily medicineComputer science

Abstract

fetched live from OpenAlex

ABSTRACT: In the last several decades, postexposure prophylaxis (PEP) with antiretroviral therapy (ART) has become an effective tool for the prevention of HIV transmission. The continuous evolution of antiretrovirals and the associated update of clinical practice guidelines create a challenge for NPs caring for patients exposed to HIV. Understanding the life cycle of HIV is of paramount importance in streamlining treatment regimens in exposed individuals. ART is a complex combination of drugs targeting different stages of the virus's life cycle within the host. NPs play an essential role in managing treatment for people exposed to HIV and following up on these patients' response and adherence to the treatment protocol. This article provides a comprehensive overview of HIV and step-by-step guidance for NPs treating patients who have been exposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.051
GPT teacher head0.390
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe Nurse PractitionerSame topicHIV/AIDS Research and InterventionsFrench-language works237,207