Review article: Managing bone complications after kidney transplantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chronic kidney disease mineral and bone disorder (CKD-MBD) describes the laboratory, bone and vascular abnormalities that exist in patients with CKD stages 3-5D and that may persist after transplantation. Persisting abnormalities of bone turnover and abnormal mineralization, together with bone mineral density (BMD) loss from glucocorticoids, may all predispose to a loss of structural integrity and increased fracture risk in kidney and kidney pancreas recipients. Vitamin D, calcitriol, calcitonin and bisphosphonates have all been used to preserve BMD following transplantation, despite a lack of safety data and the potential for some of these drugs to cause harm. A limited number of post-transplant studies utilizing these drugs have not yet documented improved fracture prevention or fracture-related mortality and have not considered allocation based on risk factors for fracture or markers of bone turnover. Targeted allocation of the available therapies based on a stratification of risk appears warranted. This might be achieved using an algorithm incorporating BMD, X-ray evaluation, laboratory investigations including bone turnover markers and the assessment of standard fracture risk factors at the time of and soon after transplantation. This approach, which is similar to protocols used in the general population, may result in more effective management of patients and fewer adverse effects such as adynamic bone disease. Although BMD is a surrogate for fracture risk in the general population it is not validated in this transplant population. Consequently, such an approach should be confirmed by studies that include bone biopsy data and an evaluation of patient level outcomes.
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.
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) | 0.002 | 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 it