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Record W3203700987 · doi:10.5114/ceh.2021.109228

Metabolic associated fatty liver disease and adverse maternal and fetal outcomes: a systematic review and meta-analysis

2021· review· en· W3203700987 on OpenAlexaboutno aff
Adinda Dyah, Rahadina Rahadina

Bibliographic record

VenueClinical and Experimental Hepatology · 2021
Typereview
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsnot available
Fundersnot available
KeywordsFatty liverMeta-analysisMedicineFetusDiseaseAdverse effectPregnancyObstetricsIntensive care medicineInternal medicineBiology

Abstract

fetched live from OpenAlex

Aim of the study: Metabolic-associated fatty liver disease (MAFLD) is now the most prevalent chronic liver disease in the world. Its prevalence in women of reproductive age is approximately 10%. Due to its high prevalence in this specific population, it is important to investigate adverse maternal and fetal outcomes caused by MAFLD during pregnancy. We aimed to summarize the association between MAFLD and adverse maternal and fetal outcomes. Material and methods: A search was conducted on PubMed and ProQuest from inception to April 1, 2021, for studies assessing the association between MAFLD and adverse maternal and fetal outcomes. The quality of included studies was assessed using the Newcastle-Ottawa scale (NOS). We analyzed the pooled odds ratios (ORs) with 95% confidence intervals (CIs) using a fixed and random-effects model. Heterogeneity was assessed using I 2 . Results: Six studies comprising 20,535,994 (5,964 MAFLD) pregnant women were included. The quality of studies ranged from 6 to 8 stars. MAFLD was significantly associated with increased risk of dysglycemia (OR = 3.65, 95% CI = 2.47-5.39), pregnancy-associated hypertension (OR = 3.27, 95% CI = 2.75-3.88), cesarean section (OR = 2.78, 95% CI = 1.60-4.83), and preterm birth (OR = 1.70, 95% CI = 1.37-2.10) but not large for gestational age (OR = 1.69, 95% CI = 0.64-4.45).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0140.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.167
GPT teacher head0.440
Teacher spread0.273 · 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 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

Citations14
Published2021
Admission routes1
Has abstractyes

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