Serosorting and recreational drug use are risk factors for diagnosis of genital infection with chlamydia and gonorrhoea among HIV-positive men who have sex with men: results from a clinical cohort in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Rates of chlamydia and gonorrhoea have been rising in urban centres in Canada, particularly among HIV-positive men who have sex with men (MSM). Our objective was to identify behavioural risk factors for diagnosis with chlamydia and gonorrhoea in this population, with a focus on the HIV status of sexual partners. METHODS: The OHTN Cohort Study follows people in HIV care across Ontario. We restricted the analysis to 1997 MSM who completed questionnaires in 2010-2013 at one of seven clinics that submit all chlamydia and gonorrhoea tests to the provincial public health laboratory; we obtained test results via record linkage. We estimated cumulative incidences using Kaplan-Meier methods and identified risk factors for diagnosis of a composite outcome (chlamydia or gonorrhoea infection) using Cox regression. RESULTS: At follow-up, there were 74 new chlamydia/gonorrhoea diagnoses with a 12-month cumulative incidence of 1.7% (95% CI 1.1% to 2.2%). Risk factors for chlamydia/gonorrhoea diagnosis were: 5+ HIV-positive partners (HR=3.3, 95% CI 1.4 to 7.8; reference=none) and recreational drug use (HR=2.2, 95% CI 1.2 to 3.9). CONCLUSIONS: Heightened risks with recreational drug use and multiple HIV-positive partners suggest that chlamydia/gonorrhoea may have achieved high prevalence in certain sexual networks among HIV-positive MSM. Interventions to promote safer sex and timely testing among MSM are needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".