P1-S2.52 Risk factors for STIs among MSM attending a sexually transmitted disease clinic in Montreal, Canada
Bibliographic record
Abstract
Background Often STIs are not diagnosed and not treated because people don't have access to appropriate healthcare screening facilities and to care. At Clinique l'Actuel (Montreal, Canada) we developed Gay Screen Clinic (GSC) as a new concept giving rapid access to men who have sex with men (MSM) to an appointment for STI screening. We then assessed the extend and risk factors of STIs in a population of men having sex with men (MSM) attending the GSC. Methods We did a retrospective analysis of the last 1000 attendees to the GSC at Clinique l'Actuel in 2009−2010. Multivariable analyses were conducted to identify the factors associated with history of STIs. Results Participants were all MSM with a mean age of 32 years (ranged from 18 to 70 y). In total, 50% (n=506) of them self reported history of STIs and 236(24%) of them had a positive sexual health screen at this visit. STI diagnoses included genital herpes (n=105, 14 %), condylomes (n=79, 8%), syphilis (n=43, 5%), chlamydia infection (n=32, 3%), HIV (n=10, 1%), Gonorrhoea (n=9, 1%) and HCV (n=5, 1%). 32% of the attendees had sexual relations in bath houses and 43% with anonymous contacts. In multivariate analyses, past history of STI was significantly associated with higher age (OR=1.02, p=0.001), higher number of sexual partners in the last 12 months (OR=1.02, p=0.015), having sexual contact in bath houses (OR=1.46, p=0.021) and with unknown partners met through internet or in backrooms (OR=1.53, p=0.004), using recreational drugs (OR=2.01, p=0.001) and having only male partners (OR=1.60, p=0.023) rather than male and female sexual partners. Conclusions STIs were common among non HIV MSM attending the GSC in Montreal. Even after many years of prevention campaign MSM still have high risk sexual behaviour. Physician should routinely enquire about drug use of their patients in order to prevent new STIs. Targeting specific sexual networks is needed to be more effective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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".