Calcium Supplementation for Preeclampsia Prevention: Estimated Risk of Excessive Intake in Ethiopian Women
Bibliographic record
Abstract
The WHO recommends supplementation with 1.5–2.0 g/d elemental calcium (Ca) for pregnant women in settings with low dietary Ca intake, to help prevent preeclampsia. There is insufficient guidance on implementing this recommendation and avoiding risk of excessive Ca intake at the population level. We investigated usual dietary Ca intake of Ethiopian women of childbearing age and simulated the impact of different supplementation doses. In a nationally‐representative sample (n= 7834), 24‐hr dietary recall data was analyzed to estimate usual dietary Ca intake and the proportions of women below the estimated average requirement (EAR ) or above the upper tolerable limit (UL) at different supplementation dosages. An EAR of 1.1 and 0.8 g/d and UL of 3.0 and 2.5 g/d was used for women 15‐18 and 19‐45 y, respectively. Mean Ca intake was 506.4 mg/d ± 263.6; 96.6% of women 15‐18y and 88.4% overall had inadequate intake. In simulations, supplementing with 1.0, 1.5, and 2.0 g/d would allow >99% of women to meet the EAR. The 1.5 and 2.0 g/d supplemental doses would result in intakes above the UL in 4.9% and 38.4% of all women, respectively. At 1.5g/d, only 0.6% of women aged 15‐18y would exceed the UL. At the 1.0g/d dose of Ca, the overall prevalence of excessive intake would be below the target of 2.5% (0.74%). Thus, with high adherence, a lower dose of 1.0 g/d would allow >99 % of Ethiopian women meet the EAR and could be safer than the recommended 1.5‐2.0 g/d. Similar investigation of potential risks of Ca supplementation using 1.5‐2.0 g/d is warranted in countries considering this intervention. Funded by The Micronutrient Initiative.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".