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Record W1928729898 · doi:10.1089/apc.2015.0014

Transitioning to HIV Pre-Exposure Prophylaxis (PrEP) from Non-Occupational Post-Exposure Prophylaxis (nPEP) in a Comprehensive HIV Prevention Clinic: A Prospective Cohort Study

2015· article· en· W1928729898 on OpenAlexaff
Reed Siemieniuk, Nirojini Sivachandran, Pauline Murphy, Andrea Sharp, Christine Walach, Tania Placido, Isaac I. Bogoch

Bibliographic record

VenueAIDS Patient Care and STDs · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity Health NetworkToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCandidacyPre-exposure prophylaxisMen who have sex with menFamily medicinePost-exposure prophylaxisHuman immunodeficiency virus (HIV)Logistic regressionCohortGuidelineInternal medicine

Abstract

fetched live from OpenAlex

The uptake of pre-exposure prophylaxis (PrEP) for HIV prevention remains low. We hypothesized that a high proportion of patients presenting for HIV non-occupational post-exposure prophylaxis (nPEP) would be candidates for PrEP based on current CDC guidelines. Outcomes from a comprehensive HIV Prevention Clinic are described. We evaluated all patients who attended the HIV Prevention Clinic for nPEP between January 1, 2013 and September 30, 2014. Each patient was evaluated for PrEP candidacy based on current CDC-guidelines and subjectively based on physician opinion. Patients were then evaluated for initiation of PrEP if they met guideline suggestions. Demographic, social, and behavioral factors were then analyzed with logistic regression for associations with PrEP candidacy and initiation. 99 individuals who attended the nPEP clinic were evaluated for PrEP. The average age was 32 years (range, 18-62), 83 (84%) were male, of whom 46 (55%) men who had have sex with men (MSM). 31 (31%) met CDC guidelines for PrEP initiation, which had very good agreement with physician recommendation (kappa=0.88, 0.78-0.98). Factors associated with PrEP candidacy included sexual exposure to HIV, prior nPEP use, and lack of drug insurance (p<0.05 for all comparisons). Combining nPEP and PrEP services in a dedicated clinic can lead to identification of PrEP candidates and may facilitate PrEP uptake. Strategies to ensure equitable access of PrEP should be explored such that those without drug coverage may also benefit from this effective HIV prevention modality.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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

Citations29
Published2015
Admission routes1
Has abstractyes

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