Sexually transmitted infections and bacterial vaginosis and preterm birth in pregnant people living with HIV: A population-based cohort study
Bibliographic record
Abstract
Background While individual sexually transmitted infections are linked with preterm births, their synergistic impact among pregnant people living with HIV (PLWH) remain unclear. We aimed to identify the impact of antenatal sexually transmitted infections and bacterial vaginosis on preterm birth in PLWH. Methods We completed a population-based cohort study using the British Columbia Perinatal HIV Surveillance Database, capturing all births in PLWH from January 1997 to December 2022. Univariate risk factors for preterm birth were identified using chi-squared tests, Fisher’s exact tests and t-tests, followed by a multivariate logistic regression analysis. Results Of 578 singleton pregnancies, 111 (19.2%) had preterm births, of which 34 (31%) delivered before 34 weeks gestational age. In our population, 11% were identified with a sexually transmitted infection or bacterial vaginosis (STIBV) in pregnancy. The preterm birth rate in PLWH with antenatal STIBV was 37% compared to 17% in PLWH without STIBV (OR: 2.18; 95% CI (1.50 – 3.16); p = .0003). Preterm deliveries were more common in individuals with concurrent Hepatitis C (OR: 2.42; p < .0001), antenatal diagnosis of Chlamydia trachomatis (OR 2.17; p = .036), Trichomonas vaginalis (OR: 2.78; p < .001) and bacterial vaginosis (OR: 2.15; p = .003). After adjusting for ethnicity, history of preterm birth, substance use, concurrent Hepatitis C, CD4 count and viral suppression at delivery, STIBV remains an independent risk factor (OR: 2.09; 95% CI: 1.04 – 4.19; p = .039). Conclusion Among PLWH, antenatal screening for sexually transmitted infections and bacterial vaginosis can identify individuals at the highest risk of preterm birth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".