Effect of high-dose folic acid supplementation on the prevention of preeclampsia in twin pregnancy
Bibliographic record
Abstract
Objective To determine the efficacy of high-dose folic acid for the prevention of preeclampsia in twin pregnancies.Methods Secondary analysis of a randomized controlled trial in 70 obstetrical sites in Argentina, Australia, Canada, Jamaica, and the UK between 2011 and 2015. Eligible women pregnant with twins who were aged 18 y or older and between 8 and 16 completed weeks’ gestation were randomized between to receive daily high-dose folic acid (4.0–5.1 mg) or placebo. The primary outcome was preeclampsia, presenting as hypertension after 20 weeks’ gestation with significant proteinuria. Secondary outcomes included severe preeclampsia, preterm birth, and adverse fetal and neonatal outcomes.Results Of 2464 participants randomized between 18 April 2011 and 14 December 2015, 462 (18.8%) had a confirmed twin pregnancy. Thirty-four of these participants withdrew consent or did not have primary outcome data available, and 428 women were analyzed. The rate of preeclampsia was significantly higher in the folic acid group compared to the placebo group in crude analyses (17.2 versus 9.9%; relative risk 1.75 [95% CI 1.06–2.88], p = .029). Multivariable analyses attenuated this effect, rendering it not statistically significant (RR 1.58 [95% CI 0.95–2.63], p = .079).Conclusion High-dose folic acid supplementation was not significantly associated with preeclampsia in a subgroup of twin pregnancies. Although a suggested elevated risk cannot be confirmed, these results may help to gain novel insights in the etiology of preeclampsia, which continues to be poorly understood.Clinical trial registration ClinicalTrials.gov NCT01355159.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".