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Record W2993335868 · doi:10.1371/journal.pmed.1002988

Antenatal magnesium sulphate and adverse neonatal outcomes: A systematic review and meta-analysis

2019· review· en· W2993335868 on OpenAlexaff
Rehana A Salam, Deepak Manhas, Anne Synnes, Philippa Middleton, Maria Makrides, Caroline A Crowther

Bibliographic record

VenuePLoS Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsUniversity of British Columbia
FundersCerebral Palsy AllianceCerebral Palsy Alliance Research Foundation
KeywordsMedicineMeta-analysisEclampsiaRelative riskRandomized controlled trialPlaceboAdverse effectCochrane LibraryPediatricsConfidence intervalObstetricsPregnancyInternal medicineAlternative medicine

Abstract

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BACKGROUND: There is widespread, increasing use of magnesium sulphate in obstetric practice for pre-eclampsia, eclampsia, and preterm fetal neuroprotection; benefit for preventing preterm labour and birth (tocolysis) is unproven. We conducted a systematic review and meta-analysis to assess whether antenatal magnesium sulphate is associated with unintended adverse neonatal outcomes. METHODS AND FINDINGS: CINAHL, Cochrane Library, LILACS, MEDLINE, Embase, TOXLINE, and Web of Science, were searched (inceptions to 3 September 2019). Randomised, quasi-randomised, and non-randomised trials, cohort and case-control studies, and case reports assessing antenatal magnesium sulphate for pre-eclampsia, eclampsia, fetal neuroprotection, or tocolysis, compared with placebo/no treatment or a different magnesium sulphate regimen, were included. The primary outcome was perinatal death. Secondary outcomes included pre-specified and non-pre-specified adverse neonatal outcomes. Two reviewers screened 5,890 articles, extracted data, and assessed risk of bias following Cochrane Handbook and RTI Item Bank guidance. For randomised trials, pooled risk ratios (RRs) or mean differences, with 95% confidence intervals (CIs), were calculated using fixed- or random-effects meta-analysis. Non-randomised data were tabulated and narratively summarised. We included 197 studies (40 randomised trials, 138 non-randomised studies, and 19 case reports), of mixed quality. The 40 trials (randomising 19,265 women and their babies) were conducted from 1987 to 2018 across high- (16 trials) and low/middle-income countries (23 trials) (1 mixed). Indications included pre-eclampsia/eclampsia (24 trials), fetal neuroprotection (7 trials), and tocolysis (9 trials); 18 trials compared magnesium sulphate with placebo/no treatment, and 22 compared different regimens. For perinatal death, no clear difference in randomised trials was observed between magnesium sulphate and placebo/no treatment (RR 1.01; 95% CI 0.92 to 1.10; 8 trials, 13,654 babies), nor between regimens. Eleven of 138 non-randomised studies reported on perinatal death. Only 1 cohort (127 babies; moderate to high risk of bias) observed an increased risk of perinatal death with >48 versus ≤48 grams magnesium sulphate exposure for tocolysis. No clear secondary adverse neonatal outcomes were observed in randomised trials, and a very limited number of possible adverse outcomes warranting further consideration were identified in non-randomised studies. Where non-randomised studies observed possible harms, often no or few confounders were controlled for (moderate to high risk of bias), samples were small (200 babies or fewer), and/or results were from subgroup analyses. Limitations include missing data for important outcomes across most studies, heterogeneity of included studies, and inclusion of published data only. CONCLUSIONS: Our findings do not support clear associations between antenatal magnesium sulphate for beneficial indications and adverse neonatal outcomes. Further large, high-quality studies (prospective cohorts or individual participant data meta-analyses) assessing specific outcomes, or the impact of regimen, pregnancy, or birth characteristics on these outcomes, would further inform safety recommendations. PROSPERO: CRD42013004451.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.037
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0210.033
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.112
GPT teacher head0.354
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations81
Published2019
Admission routes1
Has abstractyes

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