Comparative effectiveness and safety of nifedipine and magnesium sulfate as treatment options for preterm birth: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Preterm birth (PTB) is a major cause of neonatal morbidity and mortality worldwide. Effective use of tocolytic agents may improve perinatal outcomes. This study aims to compare the effectiveness and safety of nifedipine and magnesium sulfate in the treatment of PTB. DESIGN: A systematic review and meta-analysis. DATA SOURCES: China National Knowledge Infrastructure, China Science and Technology Journal Database, WanFang, PubMed, Embase, Web of Science and Cochrane were searched from inception to 1 December 2024. ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) and cohort studies that compare the efficacy and safety of magnesium sulfate versus nifedipine in treating PTB. DATA EXTRACTION AND SYNTHESIS: Two researchers independently screened studies and extracted data. Risk of bias was assessed using the Cochrane risk-of-bias assessment tool for RCTs and the modified Newcastle-Ottawa Scale for non-randomised studies. Meta-analysis was conducted using Review Manager V.5.4. RESULTS: In all, 50 articles were included in this review, comprising 6072 cases (n=3014 for the magnesium sulfate group; n=3058 for the nifedipine group). Compared with the magnesium sulfate group, the nifedipine group was more favourable in terms of time to onset of action and prolongation of days of gestation, as well as higher neonatal 1 min Apgar scores. The use of magnesium sulfate was associated with a higher incidence of maternal side effects, specifically tachycardia, flushing, palpitations, dizziness and nausea. In addition, the magnesium sulfate group also showed a higher incidence of neonatal respiratory distress syndrome than the nifedipine group. CONCLUSION: Compared with magnesium sulfate, nifedipine is more effective with a faster onset of action and a longer prolonging pregnancy. Additionally, nifedipine may be safer for fewer maternal side effects and better neonatal outcomes. Further studies are needed to confirm the long-term safety and efficacy of these treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".