Management of mineral and bone disorder after kidney transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Mineral and bone disorders (MBDs), inherent complications of moderate and advanced chronic kidney disease, occur frequently in kidney transplant recipients. However, much confusion exists about the clinical application of diagnostic tools and preventive or treatment strategies to correct bone loss or mineral disarrays in transplanted patients. We have reviewed the recent evidence about prevalence and consequences of MBD in kidney transplant recipients and examined diagnostic, preventive and therapeutic options to this end. RECENT FINDINGS: Low turnover bone disease occurs more frequently after kidney transplantation according to bone biopsy studies. The risk of fracture is high, especially in the first several months after kidney transplantation. Alterations in minerals (calcium, phosphorus and magnesium) and biomarkers of bone metabolism (parathyroid hormone, alkaline phosphatase, vitamin D and FGF-23) are observed with varying impact on posttransplant outcomes. Calcineurin inhibitors are linked to osteoporosis, whereas steroid therapy may lead to both osteoporosis and varying degrees of osteonecrosis. Sirolimus and everolimus might have a bearing on osteoblast proliferation and differentiation or decreasing osteoclast-mediated bone resorption. Selected pharmacologic interventions for the treatment of MBD in transplant patients include steroid withdrawal, and the use of bisphosphonates, vitamin D derivatives, calcimimetics, teriparatide, calcitonin and denosumab. SUMMARY: MBD following kidney transplantation is common and characterized by loss of bone volume and mineralization abnormalities, often leading to low turnover bone disease. Although there are no well established therapeutic approaches for management of MBD in renal transplant recipients, clinicians should continue individualizing therapy as needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".