Fractures in chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Osteoporotic fractures are common and cause increased sickness and death. Men and women with chronic kidney disease (CKD) are at particularly high risk of osteoporotic fractures. Currently, however, there are no guidelines concerning noninvasive methods to assess fracture risk in CKD. Further, approved treatments to prevent fractures in otherwise healthy men and women are only recommended for use with caution in those with CKD. This review focuses on the recent data that support the use of noninvasive methods to assess fracture risk in CKD and highlights new therapies that could be used in fracture prevention in CKD. RECENT FINDINGS: Data from prospective studies demonstrate that low bone mineral density predicts fracture in CKD patients. Post-hoc analyses demonstrate that agents approved for the treatment of postmenopausal osteoporosis (bisphosphonates, denosumab and teriparatide) when given to those with CKD are well tolerated and potentially efficacious with respect to fracture risk reduction. SUMMARY: To date, patients, and nephrologists taking care of them, have largely ignored fracture risk assessment and treatment in CKD. This should change given recent data. Further studies are needed, specifically bone histomorphometric studies, which will increase our understanding of CKD-mineral bone disease (MBD) pathophysiology, and randomized clinical trials of therapy in patients with CKD.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".