Effect of vitamin D on bone mineral density of elderly patients with osteoporosis responding poorly to bisphosphonates
Bibliographic record
Abstract
BACKGROUND: Bisphosphonates are indicated in the prevention and treatment of osteoporosis. However, bone mineral density (BMD) continues to decline in up to 15% of bisphosphonate users. While randomized trials have evaluated the efficacy of concurrent bisphosphonates and vitamin D, the incremental benefit of vitamin D remains uncertain. METHODS: Using data from the Canadian Database of Osteoporosis and Osteopenia (CANDOO), we performed a 2-year observational cohort study. At baseline, all patients were prescribed a bisphosphonate and counseled on vitamin D supplementation. After one year, patients were divided into two groups based on their response to bisphosphonate treatment. Non-responders were prescribed vitamin D 1000 IU daily. Responders continued to receive counseling on vitamin D. RESULTS: Of 449 patients identified, 159 were non-responders to bisphosphonates. 94% of patients were women. The mean age of the entire cohort was 74.6 years (standard deviation = 5.6 years). In the cohort of non-responders, BMD at the lumbar spine increased 2.19% (p < 0.001) the year after vitamin D was prescribed compared to a decrease of 0.55% (p = 0.36) the year before. In the cohort of responders, lumbar spine BMD improved 1.45% (p = 0.014) the first year and 1.11% (p = 0.60) the second year. The difference between the two groups was statistically significant the first year (p < 0.001) but not the second (p = 0.60). Similar results were observed at the femoral neck but were not statistically significant. CONCLUSION: In elderly patients with osteoporosis not responding to bisphosphonates, vitamin D 1000 IU daily may improve BMD at the lumbar spine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".