Total calcium intake is associated with cortical bone mineral density in a cohort of postmenopausal women not taking estrogen.
Bibliographic record
Abstract
BACKGROUND: The optimal intake of calcium and vitamin D for postmenopausal women not taking estrogen is not known. Further, there are indications that excess vitamin A as retinol might be detrimental to bone. OBJECTIVE: We determined whether dietary intakes of calcium and vitamin D were important for maintaining cortical and trabecular bone mineral density (BMD). We also determined whether nutrient supplements increased retinol intake to a level that would reduce BMD. DESIGN: This was a cross-sectional study of 58 women, age 45-75 years. Dietary intakes and lifestyle factors were assessed by retrospective questionnaires. BMD at the whole body, lumbar spine, and proximal femur (including neck, trochanter, and Wards) was measured using dual energy x-ray absorptiometry (DXA) bone densitometry. RESULTS: There were significant (p < 0.05) positive correlations between total calcium intake and BMD at all sites except spine. At the trochanter, the correlation between total vitamin D and BMD was significant while that between total retinol and BMD showed a trend (p < 0.10). In a stepwise multiple regression, a significant proportion of variance of BMD was accounted for by years since menopause (8.0 to 36.2 %) and body weight (14.5 to 27.1%) at most bone sites. Adding total calcium intake (food + supplements) into the model further accounted for a significant proportion of variance of BMD at cortical bone sites such as hip, femoral neck, Wards, and total body ( 5.2 - 8.4 %). There was no dietary calcium effect on BMD at the spine. CONCLUSION: The positive effect of total calcium intake on cortical BMD of postmenopausal women not taking estrogen suggests that supplemental calcium use is critical for maintaining bone mass. Increased retinol intake from nutrient supplements had no adverse effect on BMD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".