An evidence review and nutritional conceptual framework for pre-eclampsia prevention
Bibliographic record
Abstract
Pre-eclampsia is a serious complication of pregnancy, and maternal nutritional factors may play protective roles or exacerbate risk. The tendency to focus on single nutrients as a risk factor obscures the complexity of possible interactions, which may be important given the complex nature of pre-eclampsia. An evidence review was conducted to compile definite, probable, possible and indirect nutritional determinants of pre-eclampsia to map a nutritional conceptual framework for pre-eclampsia prevention. Determinants of pre-eclampsia were first compiled through an initial consultation with experts. Second, an expanded literature review was conducted to confirm associations, elicit additional indicators and evaluate evidence. The strength of association was evaluated as definite relative risk (RR) < 0·40 or ≥3·00, probable RR 0·40-0·69 or 1·50-2·99, possible RR 0·70-0·89 or 1·10-1·49 or not discernible RR 0·90-1·09. The quality of evidence was evaluated using Grading of Recommendations, Assessment, Development and Evaluation. Twenty-five nutritional factors were reported in two umbrella reviews and twenty-two meta-analyses. Of these, fourteen were significantly associated with pre-eclampsia incidence. Higher serum Fe emerged as a definite nutritional risk factors for pre-eclampsia incidence across populations, while low serum Zn was a risk factor in Asia and Africa. Maternal vitamin D deficiency was a probable risk factor and Ca and/or vitamin D supplementation were probable protective nutritional factors. Healthy maternal dietary patterns were possibly associated with lower risk of developing pre-eclampsia. Potential indirect pathways of maternal nutritional factors and pre-eclampsia may exist through obesity, maternal anaemia and gestational diabetes mellitus. Research gaps remain on the influence of household capacities and socio-cultural, economic and political contexts, as well as interactions with medical conditions.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".