Applying international guidelines for calcium supplementation to prevent pre-eclampsia: simulation of recommended dosages suggests risk of excess intake in Ethiopia
Bibliographic record
Abstract
OBJECTIVE: To simulate impact of Ca supplementation on estimated total Ca intakes among women in a population with low dietary Ca intakes, using WHO recommendations: 1·5-2·0 g elemental Ca/d during pregnancy to prevent pre-eclampsia. DESIGN: Single cross-sectional 24 h dietary recall data were adjusted using IMAPP software to simulate proportions of women who would meet or exceed the Estimated Average Requirement (EAR) and Tolerable Upper Intake Level (UL) assuming full or partial adherence to WHO guidelines. SETTING: Nationally and regionally representative data, Ethiopia's 'lean' season 2011. SUBJECTS: 7908, of whom 492 pregnant). RESULTS: National mean usual Ca intake was 501 (sd 244) mg/d. Approximately 89, 91 and 96 % of all women, pregnant women and 15-18 years, respectively, had dietary Ca intakes below the EAR. Simulating 100 % adherence to 1·0, 1·5 and 2·0 g/d estimated nearly all women (>99 %) would meet the EAR, regardless of dosage. Nationally, supplementation with 1·5 and 2·0 g/d would result in intake exceeding the UL in 3·7 and 43·2 % of women, respectively, while at 1·0 g/d those exceeding the UL would be <1 % (0·74 %) except in one region (4·95 %). CONCLUSIONS: Most Ethiopian women consume insufficient Ca, increasing risk of pre-eclampsia. Providing Ca supplements of 1·5-2·0 g/d could result in high proportions of women exceeding the UL, while universal consumption of 1·0 g/d would meet requirements with minimal risk of excess. Appropriately tested screening tools could identify and reduce risk to high Ca consumers. Research on minimum effective Ca supplementation to prevent pre-eclampsia is also needed to determine whether lower doses could be recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".