Calcium for Pre-eclampsia Prevention: A Systematic Review and Network Meta-analysis to Guide Personalized Antenatal Care
Bibliographic record
Abstract
(BJOG. 2022;129:1833–1843) Pre-eclampsia effects 2% to 5% of pregnancies. Health disparities are apparent with 99% of maternal pre-eclamptic deaths occurring in low- and middle-income countries (total deaths are between 30,000 and over 500,000 annually). A daily dose of aspirin (150 mg) has been shown to prevent 60% of preterm (<37 wk of gestation) pre-eclampsia, although it has not been shown to impact term pre-eclampsia (≥37 wk of gestation), accounting for over 70% of disease cases. Systematic reviews have shown that calcium supplementation can also decrease pre-eclampsia rates in parturient women [relative risk (RR) 0.45, 95% CI: 0.31-0.65] and decrease morbidity and preterm birth rates. The World Health Organization (WHO) has implemented a guideline of 1.5 to 2.0 g of calcium supplementation daily starting at 20 weeks of gestation for women not receiving adequate calcium through their diet. Questions remain regarding the appropriate calcium dose and which parturient women would benefit most from supplementation. This study aimed to determine the safety and effectiveness of calcium to help prevent pre-eclampsia, the optimal dosage amount (high dose of ≥1 g/d or low dose of <1 g/d), the optimal week of gestation that supplementation should commence, and which women would benefit the most from intervention.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".