Determinants of bone mineral density in stable kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: Kidney transplant recipients (KTR) are at increased risk for bone loss. Determinants of bone mineral density (BMD) in an unselected KTR population have not previously been described. METHODS: We conducted a cross-sectional analysis of 389 stable KTRs undergoing bone mineral densitometry assessment by dual-energy X-ray absorptiometry at the lumbar spine, total hip and femoral neck. Risk factors for osteopenia and osteoporosis were determined by t-tests or ANOVA and chi-square analysis as appropriate. Factors associated with reduced BMD were ascertained using multivariate linear regression. RESULTS: At the lumbar spine, 247 demonstrated normal BMD, 115 had osteopenia and 27 osteoporosis. Corresponding prevalence rates for the total hip and femoral neck were 222/143/24 and 178/184/27, respectively. Osteopenia or osteoporosis was more prevalent at the femoral neck than lumbar spine (p=0.002). Osteopenia or osteoporosis at the spine, hip and femoral neck were highly correlated (p<0.0001). Independent associations with reduced BMD included female sex (p<0.0001) and lower body mass index (p<0.0001) at all sites, age for total hip and femoral neck (p=0.0001), and hyperparathyroidism (p=0.036), time posttransplant (p=0.0001) for the femoral neck, with no association by renal function or 25-OH vitamin D level at any site. CONCLUSIONS: Significant bone loss in KTRs is most prevalent at the femoral neck. Identifying risk factors for specific sites may allow for earlier intervention prior to osteoporosis development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".