Vaginal Fluid Inflammatory Biomarkers and the Risk of Adverse Neonatal Outcomes in Women with PPROM
Bibliographic record
Abstract
Objective The purpose of this study was to evaluate the predictive value of vaginal fluid biomarkers for chorioamnionitis and adverse perinatal outcomes in women with preterm premature rupture of membranes (PPROM). Methods We recruited women with PPROM, without clinical chorioamnionitis, between 22 and 36 weeks' gestation. Vaginal fluid was collected on admission for the measurement of metalloproteinase-8 (MMP-8), interleukin-6 (IL-6), lactate, and glucose concentration. Placental pathology and neonatal charts were reviewed. Primary outcomes were histological chorioamnionitis and adverse neonatal neurological outcomes (intraventricular hemorrhage grade 2 or 3, periventricular leukomalacia, or hypoxic/ischemic encephalopathy). Linear regression analyses were used to adjust for gestational age at PPROM. Results Twenty-seven women were recruited at a mean gestational age of 31.6 ± 3.1 weeks, including 25 (93%) with successful collection of vaginal fluid sample. Histological chorioamnionitis and adverse neonatal neurological outcomes were observed in nine (33%) and four (15%) cases, respectively. In univariate analysis, MMP-8, IL-6, glucose, and lactate concentrations in vaginal fluid were associated with the risk of chorioamnionitis but not anymore after adjustment for gestational age at PPROM. MMP-8 concentration was the only biomarker associated with adverse neurological outcome, and it remained significant after adjustment for gestational age at PPROM (p = 0.02). Conclusion Vaginal fluid inflammatory biomarkers at admission for PPROM could predict adverse perinatal outcomes.
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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 | 0.000 |
| 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".