Three doses of vitamin D on bone mineral density in older women: a pilot study (257.4)
Bibliographic record
Abstract
Reports show that higher doses of vitamin D (vitD) intake than the recommended have no beneficial effect on BMD. In addition, both aging and weight loss reduce BMD. Our goal here is to determine whether vitD affects bone over 1 year in postmenopausal women who were counseled for weight control. Design: Fifty‐eight obese/overweight women (age, 58 ± 6 years; body mass index, 30.1 ± 3.8 kg/m2, serum 25‐hydroxyvitamin D (25OHD), 27.1 ± 4.4 ng/ml), were randomly assigned to one of 3 doses of vitD (A, 600 IU/d; B, 2000 IU/d; C, 4000 IU/d) with 1.2 Ca g/d. We analyzed 25OHD, parathyroid hormone and BMD and strength parameters by dual energy x‐ray absorptiometry and peripheral quantitative computed tomography. Results: At 1 year, serum 25OHD levels differed between groups (p < 0.01) and increased to 30.4 ± 5.2, 35.8 ± 4.5 and 41.5 ± 6.9 ng/ml, in groups A, B and C, respectively. Weight change was similar between groups (‐3.0 ± 4.1%). There was no significant BMD changes from baseline at the hip, spine or radius, but there was a decrease (p < 0.05) for total volumetric bone mineral content (BMC) (‐0.9 ± 3.4%), trabecular BMD (‐1.3 ± 3.8%) and BMC (‐2.9 ± 9.9%). Also, vitD affected cortical thickness over time (p<0.05) showing a change of ‐0.07± 0.22, +0.02 ± 0.15 and +0.09 ± 0.21 mm, for groups A, B and C, respectively. In conclusion, the vitD dose‐response on serum 25OHD levels improves cortical thickness but does not affect BMD or strength parameters. Grant Funding Source : NIH‐AJ12161
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".