The association between premature rupture of membranes (PROM) and preterm gestational age with neonatal sepsis: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Sepsis is one of the main causes of neonatal mortality. The morbidity and mortality rates due to neonatal sepsis are as high as 9-20%. Premature rupture of membranes (PROM) and preterm gestational age are among the risk factors of neonatal sepsis. Objective To evaluate for potential associations between PROM as well as preterm gestational age to neonatal sepsis by meta-analysis and systematic review. Methods A meta-analysis and systematic review were performed using literature sourced from PubMed, Cochrane, and Google Scholar according to PRISMA guidelines. We calculated the incidence of sepsis in neonates with and without PROM and premature gestational age. Journal quality was assessed according to the Newcastle-Ottawa Scale (NOS) criteria. Results From the literature search for PROM, 21 case-control studies met the inclusion criteria. Neonatal sepsis was more common in neonates who had a maternal history of PROM than in those without [OR 2.69 (95%CI 1.56 to 4.65); P<0.00001]. From the literature search for gestational age, we found 17 case-control studies that met the inclusion criteria. Neonatal sepsis was more common in preterm than term neonates [OR 2.55 (95%CI 1.61 to 4.04); P<0.00001]. Conclusion Neonates with a maternal history of PROM and/or preterm gestational age are at high risk of developing neonatal sepsis.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".