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Record W4362693113 · doi:10.1093/inthealth/ihad026

Association of maternal serum magnesium with pre-eclampsia in African pregnant women: a systematic review and meta-analysis

2023· review· en· W4362693113 on OpenAlexaboutno aff
Endalamaw Tesfa, Abaineh Munshea, Endalkachew Nibret, Solomon Tebeje Gizaw

Bibliographic record

VenueInternational Health · 2023
Typereview
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsnot available
Fundersnot available
KeywordsProteinuriaEclampsiaConfidence intervalMedicineMeta-analysisGestationMagnesiumPregnancyStrictly standardized mean differenceProspective cohort studyPreeclampsiaPathophysiologyInternal medicineObstetricsGastroenterologyChemistryKidneyBiology

Abstract

fetched live from OpenAlex

Pre-eclampsia (PE) is a pregnancy-related disorder characterized by hypertension and proteinuria occurring after 20 weeks of gestation. Several studies have been performed to determine the serum magnesium (Mg) level in PE, but most report inconclusive results. Consequently, this study was designed to resolve this controversy among African women. PubMed, Hinari, Google Scholar and African Journals Online electronic databases were searched for studies published in English. The qualities of included articles were appraised using the Newcastle-Ottawa quality assessment tool. Stata 14 software was utilized for analysis and serum Mg levels in cases and normotensive controls were compared through mean and standardized mean difference (SMD) at the 95% confidence interval (CI). In this review, we found that the mean serum Mg level was significantly reduced in cases (0.910±0.762 mmol/L) vs controls (1.167±1.060 mmol/L). The pooled SMD of serum Mg was significantly lower in cases (-1.20 [95% CI -1.64 to -0.75]). Therefore, since serum Mg is reduced in cases vs controls, we propose that Mg is involved in the pathophysiology of PE. Nevertheless, to know the exact mechanisms of Mg in PE development will require large-scale prospective studies.

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.535
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.001
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 head0.073
GPT teacher head0.381
Teacher spread0.308 · 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 designSystematic review
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

Citations10
Published2023
Admission routes1
Has abstractyes

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