A systematic review of maternal and infant outcomes following magnesium sulfate for pre‐eclampsia/eclampsia in real‐world use
Bibliographic record
Abstract
BACKGROUND: Evidence from RCTs shows that magnesium sulfate reduces the risk of seizures and mortality for women with pre-eclampsia/eclampsia. However, it has been argued that outcomes within trials may not reflect real-world outcomes with the same intervention. OBJECTIVE: To assess whether outcomes for women with pre-eclampsia/eclampsia who received magnesium sulfate in the real world were comparable to those in RCTs. SEARCH STRATEGY: EMBASE and MEDLINE were searched (January 1990-July 2010). SELECTION CRITERIA: Cohort, before-and-after, and serial cross-sectional studies were included. Participants were women with eclampsia who received magnesium sulfate or another anticonvulsant, and women with pre-eclampsia who received magnesium sulfate or no anticonvulsant. Primary outcomes were death (maternal, fetal, neonatal) or recurrent seizures. DATA COLLECTION AND ANALYSIS: Data were extracted independently by 2 reviewers. MAIN RESULTS: Six studies (1831 women with eclampsia) were included, from academic centers in Bangladesh, India, Pakistan, and Nigeria, together with 2 population-based UK studies. Magnesium sulfate for eclampsia was associated with lower risks of maternal death, recurrent seizure, and major morbidity; for pre-eclampsia, it was associated with lower risks of eclampsia. CONCLUSION: Improvements in maternal outcome with magnesium sulfate for pre-eclampsia/eclampsia in real-world use are comparable to those reported in RCTs.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".