Long-Term Changes in Bone Mineral Density in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Alterations in bone mineral metabolism occur when kidney function declines and often continue after transplantation. We investigated long-term changes in bone mineral density (BMD) among kidney transplant recipients undergoing routine clinical BMD monitoring and management. METHODS: We identified adults receiving a kidney transplant in the province of Manitoba, Canada (1996-2011) who had greater than or equal to 2 posttransplant dual energy X-ray absorptiometry examinations. Bone mineral density was expressed as Z scores (standard deviation above/below sex-matched and age-matched reference data). The main outcome was the change in BMD. RESULTS: A total of 326 kidney transplant recipients were included (mean age, 45 years; 61% men). Recipients were followed up for an average of 8.2 years (766 follow-up dual energy X-ray absorptiometry measurements). At baseline (first scan; median, 0.5 years after transplantation), bone density was slightly below average for age and sex (mean Z scores: lumbar spine, -0.4 ± 1.6; femoral neck, -0.7 ± 1.1; total hip, -0.7 ± 1.1). At the second scan (mean, 2.7 years after first scan), mean bone density Z scores have increased (lumbar spine, -0.2 ± 1.6; femoral neck, -0.6 ± 1; total hip, -0.6 ± 1.1; matched, P < 0.01 at all sites). The only factor associated with a significant BMD change at all sites was osteoporosis treatment (BMD increase). Even after restricting the analysis to recipients who had not received osteoporosis treatment, final mean bone density (mean, 8.2 years after first scan) was average for age and sex (lumbar spine, +0.7 ± 1.6; femoral neck, -0.1 ± 1.1; total hip, 0.0 ± 1.1). CONCLUSION: With routine BMD monitoring and management, posttransplant bone density typically remains stable or improves with mean values that are average for age and sex.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".