Comparison of Magnesium Sulfate and Nifedipine in Prevention of Preterm Labor
Bibliographic record
Abstract
Introduction: Fetal and neonatal complications are more common in premature than full term pregnancy. Treatment of preterm labor and postpone delivery increases neonatal survival and better quality of life and reduces health care costs for premature infants. This study aimed to compare the effects of Nifedipine and Magnesium sulfate in arresting preterm labor and to adverse the effects of these drugs. Materials and Methods: This randomized and clinical trial study was performed on 100 pregnant women who were hospitalized for preterm labor pain. The participants were pregnant women with the gestational age of 28 to 34 weeks and with a single pregnancy and symptoms of preterm were studied. They were randomly divided into two equal groups. After not suppressing the pain by fluid therapy, in the first group Magnesium sulfate infused injection (N=50) was performed, while in the second group, oral Nifedipine were given. The research uses SPSS software (version 20) statistical software issue 20 to analyze the result of tests with descriptive statistical methods including independent T test and chi square test. Results: Mean maternal age, gestational age, parity converted Magnesium sulfate and Nifedipine group had no significant difference in statistical analysis. Delivery was delayed more than 48 hours in 48% (24 person) of cases in the Magnesium sulfate group and in 72% (36 person) in Nifedipine group (p=0.03). A statistically significant difference in response to treatment was more in group of Nifedipine. Conclusion: The results showed that Nifedipine is more effective than Magnesium sulfate in postponing delivery (more than 48 hours), producing minimal side effect, having adequate price and applying an easy use method. Therefore, Nifedipine, as a tocolytic, can be a good substitute for Magnesium sulfate in preterm labor treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".