No association between early antiretroviral therapy during pregnancy and plasma levels of angiogenic factors: a cohort study
Bibliographic record
Abstract
Abstract Background: Early antiretroviral therapy (ART) during pregnancy has dramatically reduced the risk of perinatal HIV transmission. However, studies have shown an association between premature delivery and the use of ART during pregnancy (particularly protease inhibitor (PI)-based therapies), which could be explained by placental dysfunction. The objective of this study was to evaluate the association of ART (class, duration of exposure and time of initiation) with placental function by using angiogenic factors placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) as biomarkers. Methods: Clinical and biological data from 159 pregnant women living with HIV were analyzed. Levels of each biomarker were measured in the first and second trimester of pregnancy. After logarithmic transformation, we compared these using generalized estimating equations according to (a) the type of ART; (b) the duration of exposure to ART; and (c) the time of initiation of ART. Results: After adjusting for variables such as ethnicity, maternal age, gestational age, body mass index, parity, smoking status, and sex of the fetus, we found no significant association between the class of ART (PI-based or not) and serum concentrations of PlGF or sFlt-1. Furthermore, no significant association was found between biomarker levels and the duration of ART exposure or the timing of ART initiation (pre- or post-conception). Conclusions: This study suggests that first and second trimester angiogenic factor levels are not significantly associated with ART, regardless of the duration or type (with or without PI). These observations seem reassuring when considering the use of ART during early pregnancy.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".