Bibliographic record
Abstract
OBJECTIVES: To determine the aetiology of urethritis in Bangui, Central African Republic. METHODS: 410 men presenting with urethral discharge and 100 asymptomatic controls were enrolled. Urethral swabs were obtained and processed by gonococcal culture and polymerase chain reaction for the detection of Neisseria gonorrhoeae, Chlamydia trachomatis, Mycoplasma genitalium, Trichomonas vaginalis, and Ureaplasma urealyticum. RESULTS: In multivariate analyses, M genitalium and C trachomatis were significantly associated with urethral discharge when comparing cases of non-gonococcal urethritis (NGU) with controls. T vaginalis was also more common in cases than in controls, but this reached statistical significance only among cases in whom N gonorrhoeae was also detected. U urealyticum was not associated with urethritis. The gonococcus was found in 69% of cases of urethral discharge. M genitalium was the predominant pathogen in patients with NGU, being found in 42% (53/127) of such patients while C trachomatis was found in only 17% (22/127). T vaginalis was found in 18% (23/127) of patients with NGU, but also in 15% (43/283) of patients with gonococcal urethritis, and two thirds of patients with T vaginalis also had the gonococcus. Multiple infections were common. M genitalium caused a syndrome similar to chlamydial urethritis, with a less severe inflammation than in gonococcal infection. No behavioural or clinical characteristic could discriminate between the various aetiological agents. CONCLUSIONS: M genitalium is more prevalent than C trachomatis and is the most common cause of NGU in BANGUI: It causes a syndrome similar to chlamydial urethritis. T vaginalis is weakly associated with urethritis, and is often found along with other pathogens.
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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 | 0.001 |
| 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".