Vitamin K supplementation during pregnancy for improving outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
To study supplementation effect of vitamin K (VK) alone or combined with other nutrients administered to pregnant women, we searched Cochrane Pregnancy and Childbirth Group's Trials Register (till 22 January 2016, updated on 28 February 2018) including other resources. Two review authors independently assessed randomised or quasi-randomised controlled trials for inclusion, data extraction, accuracy, and risk of bias. We included older trials from high-income countries (six; 21,493 women-newborns), judged mostly as high or unclear bias risk. We could not assess high-risk e.g. epileptic women, but healthy women (different gestational ages) received varying VK dosages and duration. We meta-analysed neonatal bleeding (RR 1.16, 95% CI 0.59 to 2.29; P = 0.67) and maternal plasma VK1 (MD 2.46, 95% CI 0.98 to 3.93; P = 0.001). We found many outcomes were un-assessed e.g. perinatal death, maternal bleeding, healthcare utilization. Mostly newborns were included where VK found significantly effective for e.g. serum VK (mother-newborn), maternal breast milk VK. Few trials reported neonatal adverse side effects. The GRADE evidence quality was very low i.e. neonatal bleeding, neonatal jaundice, maternal plasma VK1. The intervention was favourable for maternal sera VK1 but remained uncertain for neonatal bleeding and other outcomes. The existing literature gaps warrant future investigations on un-assessed or inadequately reported outcomes.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".