STI prevalence, incidence, and partner notification among women in a periconception HIV prevention program in Uganda
Bibliographic record
Abstract
BackgroundWe provided sexually transmitted infection (STI) screening and facilitated partner notification and treatment among women participating in a periconception HIV prevention program in southwestern Uganda to understand follow-up STI incidence.MethodsWomen at-risk for HIV exposure while planning for pregnancy completed laboratory screening for chlamydia, gonorrhea, trichomoniasis, and syphilis at enrollment and 6 months of follow-up and/or incident pregnancy; facilitated partner notification and treatment were offered for those with positive tests. We performed a logistic regression to determine correlates of follow-up STI.ResultsNinety-four participants completed enrollment STI screening with a median age of 29 (IQR 26–34); 23 (24%) had ≥1 STI. Of the 23 participants with enrollment STI(s), all completed treatment and 19 (83%) returned for follow-up; 18 (78%) reported delivering partner notification cards and discussing STIs with partner(s), and 14 (61%) reported all partners received STI treatment. Of the 81 (86%) who successfully completed follow-up STI screening, 17 (21%) had ≥1 STI. The STI incidence rate was 29.0 per 100 person-years. In univariable regression analysis, enrollment STI, younger age, less education, and alcohol consumption were all significantly associated with follow-up STI.ConclusionsWe demonstrated high enrollment and follow-up STI rates and moderate participant-reported partner treatment among women planning for pregnancy in Uganda despite partner notification and treatment. Novel STI partner notification and treatment interventions are needed to decrease the STI burden, especially among women planning for and with pregnancy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".