Does Diabetes Increase the Risk for Fractures After Solid Organ Transplantation? A Nested Case-Control Study
Bibliographic record
Abstract
UNLABELLED: To assess the risk of fractures after a solid organ transplantation among diabetic versus nondiabetic patients, we conducted a nested case-control study. Pretransplant diabetes was associated with a 2-fold increase in post-transplant fractures. INTRODUCTION: Diabetes has been associated with osteoporosis in the general population. However, among patients receiving solid organ transplantation, the association between pretransplant diabetes and post-transplant fractures is not clear, although both diabetes and fractures are prevalent among this patient population. We aimed to determine whether pretransplant diabetes increases the risk of fractures among patients receiving solid organ transplantation. MATERIALS AND METHODS: We conducted a nested case-control study in a cohort of subjects 18 years and older, enrolled in the Quebec Drug Insurance Plan, who received a first solid organ transplantation between January 1986 and December 2005. Cases had sustained a fracture between the date of discharge from the hospitalization for solid organ transplantation and the end of the study period. All remaining patients were eligible controls. The fracture date was the case index date. Cases were matched to up to four controls on the type of organ transplanted and the date of transplantation. The index date of a control patient was that of his/her matched case. Crude and adjusted ORs were obtained with univariate and multivariate conditional logistic regression models. RESULTS: The study included 238 cases and 873 controls. Pretransplant diabetes was present in 30% of the cases and 22% of the controls (crude OR: 2.16; 95% CI: 1.7-2.8). After adjusting for age, sex, previous fractures, past hyperthyroidism, hospitalization duration, use of narcotics, benzodiazepines, antidepressants, loop diuretics, thiazide diuretics, glucocorticoids, immunosuppressants, estrogens, bisphosphonates, calcium, vitamin D, and calcitonin, pretransplantation diabetes remained a significant risk factor for fractures (adjusted OR: 1.94; 95% CI: 1.5-2.6). Use of narcotics (OR: 3.0; 95% CI: 2.0-4.4) and antidepressants (OR: 1.9; 95% CI: 1.2-3.1) in the month preceding the index date and use of loop diuretics in the year preceding the index date (OR: 1.4; 95% CI: 1.1-1.9) were also associated with increased risks of fractures. CONCLUSIONS: Pretransplant diabetes seemed to significantly increase post-transplant fractures among adults receiving solid organ transplantation. Pretransplant fracture prophylaxis should be considered in these patients.
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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 | 0.004 | 0.001 |
| 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".