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Record W4383215462 · doi:10.14238/pi63.3.2023.152-61

The association between premature rupture of membranes (PROM) and preterm gestational age with neonatal sepsis: a systematic review and meta-analysis

2023· review· en· W4383215462 on OpenAlexaboutno aff
Nanda Andini, Lili Rohmawati, Erjan Fikri, Bugis Mardina

Bibliographic record

VenuePaediatrica Indonesiana · 2023
Typereview
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsnot available
Fundersnot available
KeywordsPromMedicineNeonatal sepsisPremature rupture of membranesGestational ageSepsisPediatricsObstetricsIncidence (geometry)PregnancyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.849
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.310
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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