Back

Intravaginal Magnesium Sulfate vs Intravenous Dexamethasone for Labor Acceleration: A Bayesian Adaptive Randomized Controlled Trial

Hosseini, K.; Seifi Alan, M.; Asadpoor asl, L.; Seighali, N.; Rashidi, H.; Badehnosh, B.; Rastad, H.

2025-07-29 obstetrics and gynecology
10.1101/2025.07.29.25332357 medRxiv
Show abstract

BackgroundLabor progression and pain management are critical challenges in obstetric care. While intravenous dexamethasone has shown efficacy in accelerating labor, its systemic risks limit its use. Intravaginal magnesium sulfate offers a potential alternative, but comparative studies are lacking. This study aimed to compare the efficacy and safety of these interventions for labor acceleration. MethodsA Bayesian adaptive randomized controlled trial was conducted with 150 primiparous women in the latent phase of labor, allocated to three groups: intravaginal magnesium sulfate (50%, 10cc), intravenous dexamethasone (8mg), and routine care. Primary outcomes included labor duration and Bishop score changes, while secondary outcomes assessed neonatal Apgar scores and maternal adverse effects. Bayesian and frequentist analyses were employed, with adjustments for baseline imbalances. ResultsMagnesium sulfate significantly reduced latent phase duration by 3.0 hours (95% CrI: 2.2 to 3.9) and active phase duration by 1.99 hours (1.03 to 2.99), outperforming dexamethasone (1.8 and 1.09 hours, respectively). Both interventions achieved comparable peak Bishop score improvements at 6 hours (+3.06 vs. +3.05 points). Magnesium sulfate demonstrated superior safety, avoiding systemic risks associated with dexamethasone. ConclusionsThis study suggests intravaginal magnesium sulfate may be preferable to dexamethasone for labor acceleration, offering comparable efficacy with potentially fewer systemic risks. While both treatments are effective, magnesium sulfate could be considered as a first-line option, with dexamethasone reserved for specific cases.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.