Fracture Risk in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Fractures in men and women after kidney transplantation are associated with morbidity (including acute and chronic pain), mortality, and high economic costs. METHODS: We systematically reviewed cohort studies that provided estimates on incidence and risk factors for fracture in kidney transplant recipients. We abstracted data in duplicate and assessed the methodological quality of each study on a 17-point scale (17 representing the highest quality). RESULTS: We screened 2715 articles, reviewed 81, and included 10 studies totaling 262,678 recipients (study mean, 26,268 recipients; range, 61-77,430). The average follow-up ranged from 1.7 to 5.3 years. The study quality scores ranged from 8 to 13. Fracture sites varied by study resulting in a highly variable incidence rate ranging from 3.3 to 99.6 fractures per 1000 person-years. Similarly, the 5-year cumulative incidence for fracture varied ranging from 0.85% to 27%. Common factors associated with an increased fracture risk were older age, female sex, the presence of diabetes, and receipt of dialysis before transplantation. Other less common but statistically significant risk factors were a previous history of fracture and receipt of a kidney from a deceased (vs. living) donor. CONCLUSIONS: There is poor consensus on the incidence and risk factors for fractures in kidney transplant recipients. Previous studies vary substantially in quality, fracture definitions, and the characteristics of recipients studied. Future research should clarify fracture incidence and risk, which will inform the design of future prevention trials and guide prognostication.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".