Brief Report: Low Placental Growth Factor Levels Mid-gestation Predict Small for Gestational Age in Pregnant Women With HIV
Bibliographic record
Abstract
BACKGROUND: Placental growth factor (PlGF) and soluble Fms-like tyrosine kinase 1 (sFlt-1) are angiogenic factors essential for placental and fetal growth. Associations between these factors and birth outcomes among pregnant women with HIV are limited. METHODS: PlGF and sFlt-1 levels were quantified by ELISA in plasma samples collected between gestational weeks 24-29 from 114 women (46 with HIV, 68 without HIV). PlGF and sFlt-1:PlGF ratios were assessed using cutoffs used for prediction of preeclampsia (PlGF <12 pg/mL, PlGF <100 pg/mL, sFlt-1:PlGF >85) and compared by HIV status using χ 2 testing. Logistic regression models were fit to assess associations of dichotomized PlGF and sFlt1:PlGF with preterm (<37 weeks) and small for gestational age (SGA) birth (<10 th percentile) in all participants and stratified by HIV status. RESULTS: Women with HIV were older than women without HIV. More women with HIV had low or very low PlGF levels (<100 pg/mL: 30.4% vs 7.4%, P = 0.001; <12 pg/mL: 17.4% vs 1.5%, P = 0.002) and sFlt-1:PlGF >85 (19.5% vs 2.9%, P = 0.0036) than women without HIV. Among all pregnancies, low PlGF and high sFlt-1:PlGF ratios were significantly associated with SGA (odds ratio [95% confidence interval] for PlGF <12 pg/mL: 10.3 [2.0-53], P = 0.005; PlGF <100 pg/mL: 5.9 [1.7-21], P = 0.006; sFlt-1:PlGF >85: 10.6 [2.5-46], P = 0.002), but not preterm birth. Associations remained significant after adjusting for maternal age, BMI, and elevated blood pressure. Stratification by maternal HIV status showed this association was limited to the women with HIV. CONCLUSIONS: Low PlGF levels may be a good predictive biomarker of SGA specifically for pregnant women with HIV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".