Calcium supplementation for the prevention of hypertensive disorders of pregnancy: current evidence and programmatic considerations
Bibliographic record
Abstract
Most low- and middle-income countries present suboptimal intakes of calcium during pregnancy and high rates of mortality due to maternal hypertensive disorders. Calcium supplementation during pregnancy is known to reduce the risk of these disorders and associated complications, including preeclampsia, maternal morbidity, and preterm birth, and is, therefore, a recommended intervention for pregnant women in populations with low dietary calcium intake (e.g., where ≥25% of individuals in the population have intakes less than 800 mg calcium/day). However, this intervention is not widely implemented in part due to cost and logistical issues related to the large dose and burdensome dosing schedule (three to four 500-mg doses/day). WHO recommends 1.5-2 g/day but limited evidence suggests that less than 1 g/day may be sufficient and ongoing trials with low-dose calcium supplementation (500 mg/day) may point a path toward simplifying supplementation regimens. Calcium carbonate is likely to be the most cost-effective choice, and it is not necessary to counsel women to take calcium supplements separately from iron-containing supplements. In populations at highest risk for preeclampsia, a combination of calcium supplementation and food-based approaches, such as food fortification with calcium, may be required to improve calcium intakes before pregnancy and in early gestation.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".