Low prevalence of cervical infections in women with vaginal discharge in west Africa: implications for syndromic management
Bibliographic record
Abstract
OBJECTIVES: To measure prevalence and risk factors for cervical infections among a large sample of women consulting for vaginal discharge in west Africa and to evaluate its syndromic management through a two visit algorithm. METHODS: In 11 health centres in Bénin, Burkina Faso, Ghana, Guinée, and Mali 726 women who presented with a vaginal discharge without abdominal pain and who denied being a sex worker (SW) were enrolled. Cervical samples were tested for the detection of Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) with polymerase chain reaction (PCR) assays. All participants were treated with single dose (2 g) metronidazole and clotrimazole cream for 3 days. They were randomised to be told either to come back on day 7 only if there was no improvement in the discharge (group A), or to come back on day 7 regardless of response to treatment (group B). RESULTS: Overall, the prevalence of NG and CT was only 1.9% (14/726) and 3.2% (23/726) respectively. Risk factors previously recommended by the WHO were not associated with the presence of cervical infection, with the exception of the number of sex partners in the past 3 months. When taken together, these risk factors had a positive predictive value of only 6.4% to identify cervical infections. Prevalence of cervical infection was not higher in women who came back on day 7, regardless of the strategy used. Prevalence of NG/CT was lower in Ghana and Bénin (5/280, 1.8%), where comprehensive interventions for SW have been ongoing for years, than in the three other countries (27/446, 6.1%, p = 0.01). CONCLUSIONS: NG and CT infections are uncommon in west African women who consult for vaginal discharge and who are not SW. Syndromic management of vaginal discharge should focus on the proper management of vaginitis. The control of gonococcal and chlamydial infection should be redesigned around interventions focusing on sex workers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".