Circumcision status and incident herpes simplex virus type 2 infection, genital ulcer disease, and HIV infection
Bibliographic record
Abstract
OBJECTIVE: We assessed the protective effect of medical male circumcision (MMC) against HIV, herpes simplex virus type 2 (HSV-2), and genital ulcer disease (GUD) incidence. DESIGN: Two thousand, seven hundred and eighty-seven 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. METHODS: Cox regression estimated the risk ratio 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. RESULTS: HIV incidence was 1.42 per 100 person-years. Circumcision was 62% protective against HIV [risk ratio=0.38; 95% confidence interval (CI) 0.22-0.67] and did not change when controlling for HSV-2 and GUD (risk ratio=0.39; 95% CI 0.23-0.69). GUD incidence was halved among circumcised men (risk ratio=0.52; 95% CI 0.37-0.73). HSV-2 incidence did not differ by circumcision status (risk ratio=0.94; 95% CI 0.70-1.25). In the multivariable model, HIV seroconversions were tripled (risk ratio=3.44; 95% CI 1.52-7.80) among men with incident HSV-2 and seven times greater (risk ratio=6.98; 95% CI 3.50-13.9) for men with GUD. CONCLUSION: 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 | 0.005 |
| 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".