P1-S6.54 Medical male circumcision may be protective of urogenital<i>Mycoplasma genitalium</i>infection: results from a randomised trial in Kisumu, Kenya
Bibliographic record
Abstract
Background We determined the prevalence of Mycoplasma genitalium (MG) and whether this was associated with circumcision status, among men enrolled in the randomised trial of medical male circumcision (MMC) to prevent HIV acquisition in Kisumu, Kenya. Methods MG was detected in first void urine (FVU) by APTIMA transcription mediated amplification (TMA) assay. FVU and urethral swabs were assessed for Neissseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) by PCR, and for Trichomonas vaginalis (TV) by culture. HSV-2 detection was by IgG ELISA. Personal interview assessed socio-demographic and behavioural risks. All men underwent standardised medical history and physical examination. Results July through September 2010, 52 (9.9%; 95% CI: 7.3 to 12.4%) MG infections were detected among 526 men. The prevalence of NG (1.4%) and TV (2.7%) did not differ by MG status. CT prevalence was 5.8% among MG-infected men, and 0.8% among MG-uninfected men (p=0.02). CT infection at enrolment was more prevalent among MG infected men (27% vs 9% uninfected, p=0.005); NG and TV at enrolment were not associated with MG. Current urethral discharge symptoms and exam findings did not vary by MG status (1.9% positive vs 1.5% negative, p=0.80), but remote history of urethral discharge (past 6 months) was 5.8% among men with MG vs 2.1% uninfected (p=0.10). The prevalence of MG was 13.4% in uncircumcised men vs 8.2% in circumcised men (p=0.06). In multivariable logistic regression, circumcision status nearly halved the odds of MG [adjusted OR=0.54; 95% CI 0.29 to 0.99], adjusted for variables significant at the p<0.05 level: HSV-2 infection [aOR=2.05; 95% CI 1.05 to 4.00], CT infection at enrolment or at follow-up [aOR=2.69; 95% CI 1.44 to 5.02], washing the penis ≤1 h after sex [aOR=0.47; 95% CI 0.24 to 0.95]. The prevalence of MG did not differ by HIV status, age, education, marital status, number of sex partners, condom use, or sex during menses. There were no interactions with circumcision status. Conclusions Prospective study is needed to verify the potential protective association between MMC and MG. The mechanism by which MMC may protect against MG is unclear. Randomised trials have not found MMC to protect against other urethral infections (NG, CT, TV), though circumcised and uncircumcised men may have different peri-urethral bacteria and penile microbiome. The role of MG in STI morbidity, syndromic management algorithms and antibiotic regimens for men and women in this region should be evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".