<scp>HIV</scp> incidence and related risks among gay, bisexual, and other men who have sex with men in Montreal, Toronto, and Vancouver: Informing blood donor selection criteria in Canada
Bibliographic record
Abstract
BACKGROUND: An individualized behavior-based selection approach has potential to allow for a more equitable blood donor eligibility process. We collected biological and behavioral data from urban gay, bisexual, and other men who have sex with men (GBM) to inform the use of this approach in Canada. STUDY DESIGN AND METHODS: Engage is a closed prospective cohort of sexually active GBM, aged 16+ years, recruited via respondent-driven-sampling (RDS) in Montreal, Toronto, and Vancouver, Canada. Participants completed a questionnaire on behaviors (past 6 months) and tested for HIV and sexually transmitted and blood-borne infections at each visit. Rate ratios for HIV infection and predictive values for blood donation eligibility criteria were estimated by RDS-adjusted Poisson regression. RESULTS: Data on 2008 (study visits 2017-02 to 2021-08) HIV-negative participants were used. The HIV incidence rate for the three cities was 0.4|100 person-years [95%CI:0.3, 0.6]. HIV seroconversion was associated with age <30 years: adjusted rate ratio (aRR) 9.1 [95%CI:3.2, 26.2], 6-10 and >10 anal sex partners versus 1-6 aRR: 5.3 [2.1,13.5] and 8.4 [3.4, 20.9], and use of crystal methamphetamine during sex: 4.2 [1.5, 11.6]. Applying the combined selection criteria: drug injection, ≥2 anal sex partners, and a new anal sex partner, detected all participants who seroconverted (100% sensitivity, 100% negative predictive value), and would defer 63% of study participants from donating. CONCLUSION: Using three screening questions regarding drug injection and sexual behaviors in the past 6 months would correctly identify potential GBM donors at high risk of having recently contracted HIV. Doing so would reduce the proportion of deferred sexually active GBM by one-third.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".