Transitioning to HIV Pre-Exposure Prophylaxis (PrEP) from Non-Occupational Post-Exposure Prophylaxis (nPEP) in a Comprehensive HIV Prevention Clinic: A Prospective Cohort Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".