Prevalence and treatment of decreased bone density in renal transplant recipients: a randomized prospective trial of calcitriol versus alendronate
Bibliographic record
Abstract
BACKGROUND: Reduced bone mineral density (BMD) is common in long-term renal transplant recipients and results in a high incidence of fractures. The optimal therapy for these patients is not known. METHODS: Baseline BMD determinations were obtained in 211 long-term adult renal transplant recipients. One hundred and seventeen patients with a reduced BMD (T score < or = -1) were randomly assigned to treatment with alendronate and calcium (n=60) versus calcitriol and calcium (n=57). Of these, 46 and 51 patients, respectively, completed 1 year of treatment. Forty-nine patients who were not eligible or did not consent to the trial were followed prospectively. RESULTS: Reduced baseline BMD (T score < or = -1) was present in 159 (78.7%) of patients at the lumbar spine or femur. There was no significant loss of BMD in the prospectively followed patients during 2.7 years. The average lumbar BMD increased from 0.984+/-0.149 to 1.025+/-0.143 g/cm2 (P<0.001) with alendronate and from 1.014+/-0.15 to 1.034+/-0.146 g/cm2 (P=0.002) with calcitriol. BMD at the femur increased from 0.809+/-0.092 to 0.836+/-0.107 g/cm2 (P<0.001) with alendronate and from 0.830+/-0.144 to 0.857+/-0.125 g/cm2 (P=0.023) with calcitriol. CONCLUSIONS: One year of treatment with alendronate or calcitriol, both with calcium supplementation, resulted in significant increases in BMD at the lumbar spine and femur, with a trend toward alendronate being more effective at the spine (P=0.082). Further studies are needed to determine whether BMDs continue to increase after 1 year and whether there is any additional benefit to combining vitamin D and alendronate. Larger studies are needed to determine whether treatment decreases fracture rates.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".