Enhancement of Calcium/Vitamin D Supplement Efficacy by Administering Concomitantly Three Key Nutrients Essential to Bone Collagen Matrix for the Treatment of Osteopenia in Middle-Aged Women: A One-Year Follow-Up
Bibliographic record
Abstract
Two vitamins and proline (CB(6)Pro), three nutrients essential for bone collagen, were used in combination to a 1000 mg calcium/250 IU vitamin D (Ca/D) daily supplement to treat osteopenia as a preventive measure against osteoporosis later in life. Middle-aged women not using estrogen were screened for osteopenia using the WHO criteria and divided into three groups (n = 20 each): 1) placebo healthy controls with normal bone mineral density (BMD); 2) control Ca/D-treated osteopenic patients; and 3) Ca/D + CB(6)Pro-treated osteopenic patients. The three groups were comparable at baseline except for BMD. After one-year treatment, cortical diaphyseal BMD remained constant in each group, but trabecular bone loss persisted (at 5 lumbar sites) in osteopenic group 2. No further bone loss was detected in osteopenic group 3. A loss of 2% was evidenced in the placebo group at one lumbar site. Markers of bone formation (which increase in coupling to resorption) decreased significantly in both osteopenic groups. Although biomarkers of resorption did not change, hormone (PTH and 1,25(OH)(2)D(3))-induced osteoclastic activity was significantly reduced. No decline in BMD occurred at any bone site in osteopenic group 3, highlighting the importance of improving the quality of bone matrix concomitantly to mineral replacement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".