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Biomedical HIV Prevention Including Pre-exposure Prophylaxis and Opiate Agonist Therapy for Women Who Inject Drugs

2015· review· en· W234525111 on OpenAlexaff
Kimberly Page, Judith I. Tsui, Lisa Maher, Kachit Choopanya, Suphak Vanichseni, Philip A. Mock, Connie Celum, Michael Martin

Bibliographic record

VenueJAIDS Journal of Acquired Immune Deficiency Syndromes · 2015
Typereview
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute on Drug Abuse
KeywordsMedicineHuman immunodeficiency virus (HIV)OpiateAgonistPre-exposure prophylaxisPharmacologyIntensive care medicineInternal medicineVirologyMen who have sex with menSyphilis

Abstract

fetched live from OpenAlex

Women who inject drugs (WWID) are at higher risk of HIV compared with their male counterparts as a result of multiple factors, including biological, behavioral, and sociostructural factors, yet comparatively little effort has been invested in testing and delivering prevention methods that directly target this group. In this article, we discuss the need for expanded prevention interventions for WWID, focusing on 2 safe, effective, and approved, yet underutilized biomedical prevention methods: opiate agonist therapy (OAT) and oral pre-exposure prophylaxis (PrEP). Although both interventions are well researched, they have not been well examined in the context of gender. We discuss the drivers of women injectors' higher HIV risk, review the effectiveness of OAT and PrEP interventions among women, and explain why these new HIV prevention tools should be prioritized for WWID. There is substantial potential for impact of OAT and PrEP programs for WWID in the context of broader gender-responsive HIV prevention initiatives. Although awaiting efficacy data on other biomedical approaches in the HIV prevention research "pipeline," we propose that the scale-up and implementation of these proven, safe, and effective interventions are needed now.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.077
GPT teacher head0.392
Teacher spread0.315 · 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

Citations33
Published2015
Admission routes1
Has abstractyes

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