A Meta-Analysis on the Efficacy and Safety of Combined Vitamin C and E Supplementation in Preeclamptic Women
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether vitamin C and E co-supplementation of women at risk of preeclampsia can reduce maternal and neonatal disorders. METHOD: Electronic databases were searched up to May 2008 to find studies investigating pregnancy outcomes in women at risk of preeclampsia following exposure to combined vitamin C and E supplementation. The outcomes of interest were gestational hypertension, preeclampsia, preterm delivery, small for gestational age, and low birth weight. The relative risk (RR) and confidence interval (CI) for the individual studies were pooled and heterogeneity analysis was performed. RESULTS: Seven studies involving 5969 pregnant women at risk of preeclampsia were included: 2982 received vitamin C and E and 2987 received placebo. The RRs are 1.3 (95% CI of 1.08-1.57, p = 0.0066) for gestational hypertension, 0.7 (95% CI of 0.58-1.08, P = 0.1653) for preeclampsia, 1.12 (95% CI of 0.96-1.32, p = 0.141) for preterm delivery, 1.04 (95% CI of 0.94-1.15, p = 0.4789) for small for gestational age, and 1.13 (95% CI of 1.004-1.27, p = 0.0429) for low birth weight. CONCLUSION. Combined vitamin C and E supplementation not only have no potential benefit in improvement of maternal and neonatal outcome but increase the risk of gestational hypertension in women at risk of preeclampsia and low birth weight in neonates.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".