Risk Factors for First and Recurrent Fractures among Kidney Transplant Recipients
Bibliographic record
Abstract
Introduction: Kidney transplantation is associated with increased risk of bone fracture. Current literature reports widely variable fracture burden and contains limited data on risk factors for recurrent fractures. Methods: The incidence of all and major osteoporotic fractures (hip, forearm, thoracolumbar, and proximal humerus) were assessed. The risk factors for first and recurrent fractures among 1285 Canadian kidney transplant recipients transplanted between January 1, 2004, and December 31, 2013 were also identified. Results: The 10-year cumulative incidence of all fractures and major osteoporotic fractures in this population was 27.1% (95% CI: 22.5, 32.4) and 17.8% (95% CI: 13.4, 23.5), respectively. On multivariable analysis, female sex (HR = 1.64 [95% CI: 1.20, 2.26]), history of fracture (HR = 1.54 [95% CI: 1.12, 2.11]), and pretransplant diabetes (HR = 1.85 [95% CI: 1.29, 2.65]) were recipient factors found to increase the risk for any first fracture posttransplant. These risk factors persist in analysis with the time origin 3-months posttransplant, where transplant age (HR = 1.01 [95% CI: 1.00, 1.03]) and increased time on pretransplant dialysis (HR = 1.06 [95% CI: 1.00, 1.12]) also emerge as risk factors for first fracture. On multivariable shared frailty model analysis, increased risk of recurrent fractures was associated with recipient female sex (HR = 1.74 [95% CI: 1.21, 2.51]) and history of diabetes (HR = 1.76 [95% CI: 1.17, 2.66]). Discussion: The results suggested that some risk factors for first fracture may not inform risk of recurrent fractures. As such, fracture risk should be assessed accordingly to optimize long-term care and implement preventive measures.
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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) | 0.000 | 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".