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Record W4410259741 · doi:10.18502/ijph.v54i5.18626

Adverse Neonatal Outcomes in Pregnant Women with Severe Vomiting: A Meta-Analysis

2025· review· en· W4410259741 on OpenAlexaboutno aff
Shuixia Chen

Bibliographic record

VenueIranian Journal of Public Health · 2025
Typereview
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisVomitingMedicineObstetricsAdverse effectPregnancyIntensive care medicineInternal medicineBiology

Abstract

fetched live from OpenAlex

Background: We aimed to systematically evaluate the risk of negative effect for newborns born to expectant mothers with severe vomiting in terms of birth weight, premature delivery, low Apgar score, and NICU hospitalization. Methods: We conducted a systematically search for relevant studies on PubMed, Embase, Cochrane Library, and CNKI databases, using Newcastle-Ottawa Scale to evaluate research quality, and RevMan 5.3 software for meta-analysis from 2009 to 2022. The main outcome measures were: Low-birth weight, preterm delivery, low Apgar score and growth restriction. Results: In 9 studies, the risk of Low birth weight in hyperemesis pregnant women was increased, and the random effect model was OR 2.38 (95% CI 0.43 to 13.13). The heterogeneity of the study was high (I2=100%). Four studies showed an increased risk of low Apgar scores, with an OR of 2.69 (95% CI 0.30 to 24.48), and high heterogeneity (I2=95%). The risk of premature birth in 5 papers is equivalent, with an OR of 0.93 (95% CI 0.71 to 1.22) and low heterogeneity (I2=6%). The risk of growth restriction was higher in 7 papers, with an OR of 1.31 (95% CI 0.93 to 1.85) and lower heterogeneity (I2=29%). Subgroup analysis showed that heterogeneity mainly stemmed from differences in the definition of hyperemesis. Conclusion: Pregnant women with severe vomiting have a higher risk of giving birth to babies with low birth weight and low Apgar scores, and a higher risk of giving birth to babies with growth restriction, but the risk of premature birth is comparable

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.160
GPT teacher head0.409
Teacher spread0.249 · 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.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueIranian Journal of Public HealthSame topicPregnancy and Medication ImpactFrench-language works237,207