High prevalence of Chlamydia trachomatis and Neisseria gonorrhoeae infections among HIV-1 negative men who have sex with men in coastal Kenya
Bibliographic record
Abstract
OBJECTIVES: To assess the burden of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) in high-risk HIV-1 negative men who have sex with men (MSM) in Africa. METHODS: Before the start of a pre-exposure prophylaxis trial, HIV-1 negative volunteers were screened for sexually transmitted infection (STI) including CT and NG, using a highly sensitive and specific nucleic acid amplification test. Samples positive for CT by Aptima testing, were evaluated for the presence of lymphogranuloma venereum (LGV) serovars using an in-house PCR assay. All men were asked to submit a urine specimen, and all had a rectal swab collected by a clinician. Men were asked if they had dysuria, urethral or rectal discharge, or rectal pain. RESULTS: 43 HIV-1 negative MSM were screened, of whom 13 reported sex with men only; the majority (27/43) reported sex work. One volunteer had dysuria and another, rectal pain. Eleven MSM (26%, 95% CI 14% to 41%) had infections with either or both pathogens. Homosexual men had a higher prevalence of any infection than bisexual men (46% vs 17%, p=0.04), and all cases of rectal infections, including one with CT, two with NG and two with CT/NG co-infection. All patients with CT were negative for LGV. One patient with a rectal NG infection reported rectal pain. CONCLUSIONS: A remarkably high burden of STI infection was found among HIV-1 negative MSM. Most (12/13) infections, including three of four rectal NG infections, were subclinical. These findings suggest that high-risk MSM will benefit from effective STI screening in Kenya.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".