Circumcision Status and Incident HSV-2 Infection, Genital Ulcer Disease, and HIV Infection
Bibliographic record
Abstract
Objective: We assessed the protective effect of medical male circumcision (MMC) against HIV, HSV-2, and GUD incidence.\nDesign: 2784 men aged 18–24 years living in Kisumu, Kenya were randomly assigned to circumcision (n=1391) or delayed circumcision (n=1393), and assessed by HIV and HSV-2 testing and medical examinations during follow-ups at 1, 3, 6, 12, 18, and 24 months.\nMethods: Cox regression estimated the risk ratio (RR) of each outcome (incident HIV, GUD, HSV-2) for circumcision status and multivariable models estimated HIV risk associated with HSV-2, GUD and circumcision status as time-varying covariates.\nResults: HIV incidence was 1.42 per 100 person-years. Circumcision was 62% protective against HIV [RR=0.38; 95% CI: 0.22 - 0.67], and did not change when controlling for HSV-2 and GUD [RR=0.39; 95% CI: 0.23 - 0.69]. GUD incidence was halved among circumcised men [RR=0.52, 95% CI: 0.37 - 0.73]. HSV-2 incidence did not differ by circumcision status [RR=0.94; 95% CI: 0.70 - 1.25]. In the multivariable model, HIV seroconversions were tripled [RR=3.44; 95% CI: 1.52 - 7.80] among men with incident HSV-2 and 7 times greater [RR=6.98; 95% CI: 3.50 - 13.9] for men with GUD.\nConclusion: Contrary to findings from the South African and Ugandan trials, the protective effect of MMC against HIV was independent of GUD and HSV-2 and MMC had no effect on HSV-2 incidence. Determining the causes of GUD is necessary to reduce associated HIV risk, and to understand how circumcision confers protection against GUD and HIV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".