Prevalence of Vitamin D Inadequacy among Postmenopausal North American Women Receiving Osteoporosis Therapy
Bibliographic record
Abstract
PURPOSE: To evaluate serum 25-hydroxyvitamin D [25(OH)D] concentrations and factors related to vitamin D inadequacy in postmenopausal North American women receiving therapy to treat or prevent osteoporosis. METHODS: Serum 25(OH)D and PTH were obtained in 1536 community-dwelling women between November 2003 and March 2004. Multivariate logistic regression was used to assess risk factors for suboptimal (<30 ng/ml) 25(OH)D. RESULTS: Ninety-two percent of study subjects were Caucasian, with a mean age of 71 yr. Thirty-five percent resided at or above latitude 42 degrees north, and 24% resided less than 35 degrees north. Mean (sd) serum 25(OH)D was 30.4 (13.2) ng/ml: serum 25(OH)D was less than 20 ng/ml in 18%; less than 25 ng/ml in 36%; and less than 30 ng/ml in 52%. Prevalence of suboptimal 25(OH)D was significantly higher in subjects who took less than 400 vs. 400 IU/d or more vitamin D. There was a significant negative correlation between serum PTH concentrations and 25(OH)D. Risk factors related to vitamin D inadequacy included age, race, body mass index, medications known to affect vitamin D metabolism, vitamin D supplementation, exercise, education, and physician counseling regarding vitamin D. CONCLUSIONS: More than half of North American women receiving therapy to treat or prevent osteoporosis have vitamin D inadequacy, underscoring the need for improved physician and public education regarding optimization of vitamin D status in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".