Identification of Novel Risks for Nonulcerative Sexually Transmitted Infections Among Young Men in Kisumu, Kenya
Bibliographic record
Abstract
OBJECTIVES: STI prevention interventions often aim to reduce HIV incidence. Understanding STI risks may lead to more effective HIV prevention. GOAL: To identify STI risks among men aged 18-24 in Kisumu, Kenya. STUDY DESIGN: We analyzed baseline data from a randomized trial of male circumcision. Participants were interviewed for sociodemographic and behavioral risks. Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) were diagnosed by polymerase chain reaction assay and Trichomonas vaginalis (TV) by culture. The outcome for logistic regression analysis was infection with NG, CT, or TV. RESULTS: Among 2743 men, 214 (7.8%; 95% CI: 6.8%-8.8%) were infected with any STI. In multivariable analysis, statistically significant risks for infection were: living one's whole life in Kisumu (OR = 1.50; 95% CI: 1.12-2.01), preferring "dry" sex (OR = 1.47; 95% CI: 1.05-2.07), HSV-2 seropositivity (OR = 1.37; 95% CI: 1.01-1.86), and inability to ejaculate during sex (OR = 2.04; 95% CI: 1.15-3.62). Risk decreased with increasing age and education, and cleaning one's penis less than 1 hour after sex (OR = 0.51; 95% CI: 0.33-0.80). CONCLUSION: Understanding how postcoital cleaning, "dry" sex, and sexual dysfunction relate to STI acquisition may improve STI and HIV prevention.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".