Vitamin/Mineral Supplements and Calcium-Based Antacids Increase Maternal Calcium Intake
Bibliographic record
Abstract
OBJECTIVE: The contributions of over-the-counter (OTC) calcium-based antacid medications and calcium-containing vitamin/mineral supplements to total calcium intake during pregnancy, have rarely been assessed. This study estimates the contributions of calcium-based antacids and vitamin/mineral supplements to maternal calcium intake. METHODS: Over an 8-month period, a cohort of 724 prenatal class attendees (out of a possible 1100 participants) at >28 weeks gestation in Calgary, Alberta, completed an anonymous questionnaire on vitamin/mineral supplement intake and the use of calcium-based antacids. A subset of 264 women completed a self-reported calcium-modified food frequency questionnaire. RESULTS: The use of prenatal vitamins/minerals increased during pregnancy as did use of the single nutrients calcium and iron. Calcium-based antacids were used by 52% (n = 365) of pregnant women. Median intake of calcium from maternal diet alone was 1619 mg/d (mean intake, 1693 +/- 94), which rose to 2084 mg/d (mean intake, 2228 +/- 116) when diet, vitamin/mineral supplements, and antacids were considered. From diet alone, 18% had less than adequate intake (AI = 1000 mg/d) of calcium and 12% exceeded the tolerable upper intake level (UL = 2500 mg/d). Adding antacids reduced to 5% those below the AI and increased those surpassing the UL to 33%. No adverse events were reported at calcium intakes above the UL. CONCLUSIONS: Vitamin/mineral supplements and calcium-based antacids increased total maternal calcium intake, resulting in fewer women with intakes < AI but also increasing the number of those with intakes > UL. It is suggested that health care providers discuss all sources of nutrient intake with pregnant clients, as cumulative intakes may unintentionally exceed recommended levels.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".