Importance of diabetes as a risk factor for fractures after solid organ transplantation
Bibliographic record
Abstract
Background. Diabetes seems to be associated with an increased risk of fractures in the general population. We aimed to determine whether pre-transplant diabetes increases the risk of fractures among patients receiving solid organ transplantation. Methods. We conducted a nested case-control study in a cohort of subjects 18 years and older who received a first solid organ transplantation in Quebec between January 1st 1986 and July 31st 2005, and who were covered by the RAMQ drug plan at least 1 year before the transplantation and 3 months after the date of discharge from the transplantation hospitalization. Cases were subjects from the cohort who had sustained a fracture between the date of discharge from the hospitalization for transplantation and the end of the study period or the patient's death. The fracture date was the case index date. All incidental fractures were included except fractures of the skull, phalanges of the hand and foot, multiple fractures and pathological fractures, and were identified by medical service claims. Controls were matched to cases on the type of organ transplanted and on the date of the transplantation (+/- 3 months). Crude and adjusted odds ratios (OR) were obtained with univariate and multivariate conditional logistic regression models. Results. The study included 238 cases and 873 controls. Pre-transplant 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 potential confounders, pre-transplantation diabetes remained a significant risk factor for fractures (adjusted OR: 1.94, 95% CI: 1.5--2.6). Conclusion. Pre-transplant diabetes appeared to significantly increase post-transplant fractures among adults receiving solid organ transplantation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".