Characterization of an Emerging Heterosexual HIV Epidemic in Russia
Bibliographic record
Abstract
BACKGROUND: The Russian Federation is currently experiencing one of the fastest growing HIV epidemics worldwide. The objective was to identify sexual risk factors for recent heterosexually-acquired HIV infections. METHODS: A case-control study of recent HIV infection was conducted in the regions of Altaiskiy Krai, Krasnoyarskiy Krai, Saratov Oblast, and Tverskaya Oblast. Data from 166 participants who did not report recent injection drug use were analyzed (19 male cases, 22 male controls, 67 female cases, 58 female controls). Independent risk factors for HIV infection are reported as adjusted odds ratios (AOR) with 95% confidence intervals (CI). RESULTS: Risk factors were unprotected sex with an HIV-positive/status unknown regular partner (among women only: AOR 5.4, 95% CI 2.1-13); a regular sexual partner who was an injection drug user (AOR 3.6, 95% CI 1.5-8.5); 5 or more sexual partners (among men only: AOR 2.7, 95% CI 0.66-11); unprotected sex with a partner who had a diagnosed sexually transmitted infection (STI) or signs/symptoms of an STI (AOR 6.4, 95% CI 1.1-38); and undiagnosed signs/symptoms of an STI (AOR 3.4, 95% CI 1.5-7.6). CONCLUSIONS: These data provide evidence of bridging between the injecting and noninjecting populations. Concomitant STI seem to have a major role in fueling the Russian HIV epidemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".