Gay, bisexual, and other men who have sex with men accessing STI clinics: Optimizing HIV PrEP implementation
Bibliographic record
Abstract
BACKGROUND: Gay, bisexual and other men who have sex with men (gbMSM) who attend STI clinics represent an easily accessible population for promoting HIV prevention interventions. We examined characteristics of gbMSM STI clinic attendees to identify those who could most benefit from pre-exposure prophylaxis (PrEP). SETTING: GbMSM STI clinic attendees in British Columbia (BC), Canada. METHODS: A clinical electronic charting system of STI clinics in BC was used to identify gbMSM from 2004 to 2017. Incident HIV cases were defined as testers who had at least one HIV-negative test and a subsequent HIV-positive test. Seroconversion rates were calculated by risk factor variables and by year. Cox proportional hazards regression was used to identify independent predictors of HIV seroconversion. RESULTS: There were 9,038 gbMSM included, of whom 257 HIV seroconverted over the study period and 8,781 remained negative HIV testers, contributing 650.8 and 29,591.0 person-years to the analysis, respectively. The overall rate of seroconversion was 0.85 per 100 person-years (95% CI: 0.75-0.96). Incidence rates were higher among patients reporting >5 partners in the previous six months, inconsistent condom use, or having a partner living with HIV and who had a previous or concurrent diagnosis of rectal gonorrhea or rectal chlamydia. gbMSM presenting with two STIs such as rectal gonorrhea and syphilis (3.59/100 person-years [95%CI: 2.33-5.22]) or rectal chlamydia and syphilis (3.01/100 person-years [95%CI: 2.00-4.29]) had the highest incidence rates. CONCLUSION: gbMSM with preceding or concurrent rectal STI diagnoses or syphilis had higher rates of HIV seroconversion. The data support the inclusion of specific STI diagnoses as an indication for PrEP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".