Perinatal risk factors for neonatal early-onset sepsis: a meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVE: Early-onset neonatal sepsis (EONS) remains an important cause of neonatal mortality and has many risk factors, therefore, this study aimed to investigate the perinatal risk factors for EONS. METHODS: We searched CNKI, Wan Fang, VIP, CBM, PubMed, Embase, and Web of Science to compile studies regarding the incidence of neonatal early-onset sepsis, published up to 1 May 2022. To evaluate the quality of the included studies, we used the Newcastle-Ottawa Scale, and the RevMan5.3 software was used for meta-analysis. RESULTS: A total of 17 studies were included, with 1987 cases in the case group and 4814 cases in the control group. Meta-analysis showed that perinatal asphyxia or intrauterine distress (OR = 3.00, 95% CI: 2.18-4.13), amniotic fluid meconium contamination (OR = 4.51, 95% CI: 2.31-8.81), group B streptococcal (GBS) colonization in pregnant women (OR = 2.13, 95% CI: 1.48-3.05), chorioamnionitis (OR = 4.58, 95% CI: 2.61-8.05), premature rupture of membranes (OR = 2.63, 95% CI: 2.09-3.30), lower gestational age (OR = 1.31, 95% CI: 1.18-1.44), maternal urinary or reproductive tract infection (OR = 3.61, 95% CI: 2.14-6.11), perinatal fever (OR = 3.59, 95% CI: 2.25-5.71), very low birth weight (OR = 3.79, 95% CI: 2.14-6.73), and vaginal examination ≥3 times (OR = 7.95, 95% CI: 4.04-15.64) were the perinatal risk factors for EONS. CONCLUSION: Perinatal asphyxia or intrauterine distress, meconium contamination in amniotic fluid, GBS colonization in pregnant women, chorioamnionitis, premature rupture of membranes, lower gestational age, maternal urinary tract or reproductive tract infection, perinatal fever, very low birth weight, and vaginal examinations ≥3 times may increase the risk of EONS.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.052 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".