The Association Between Early Membrane Rupture, Latency, Clinical Chorioamnionitis, Neonatal Infection, and Adverse Perinatal Outcomes in Twin Pregnancies Complicated by Preterm Prelabour Rupture of Membranes
Bibliographic record
Abstract
The objective of this study was to evaluate associations between adverse outcomes in twin pregnancies and preterm prelabour rupture of membranes (PPROM). A chart review of 246 consecutive twin pregnancies with confirmed PPROM was conducted. Regression analysis (beta [natural log of the odds ratio] and odds ratio [OR]) was performed to identify independent predictors. Two hundred and forty-six twin pregnancies, 492 liveborns, and 20 neonatal deaths. Mean (SD) PPROM gestational age (GA): 31.3 (3.8) wk; delivery GA: 32.0 (3.3) wk. PPROM < 30 wk was associated with increased parity (OR: 2.66), and log (admission leukocyte count) (OR: 9.99). Shortened latency was associated with PPROM GA (beta = -0.17) and chorioamnionitis (beta = 0.95). Neonatal sepsis was predicted by lower delivery GA (OR: 2.04). Adverse perinatal outcomes were protected against by older GA at PPROM (OR 0.53) and shortened latency (OR 0.73). It was concluded that increased leukocytosis and parity implies an infectious aetiology in earlier PPROM. Increased risk for neonatal sepsis at earlier delivery GA is consistent with gestation-dependent fetal immunocompetence. Early PPROM and long latencies were associated with increased 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 | 0.009 |
| 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 | 0.000 | 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".