Post-transplant diabetes mellitus in Canadian liver and renal transplant recipients
Bibliographic record
Abstract
BACKGROUND: Post-transplant diabetes mellitus (PTDM) occurs in 10%–40% of liver and renal transplant recipients. Whether the risk factors for PTDM in liver and renal transplant recipients are similar and whether Indigenous Canadians, who have a high underlying prevalence of diabetes mellitus (DM), are at increased risk of developing PTDM have yet to be determined. OBJECTIVE: To describe and compare those variables associated with PTDM in adult Canadian liver and renal transplant recipients. METHODS: A retrospective chart review of adult liver and renal transplant recipients attending four transplant follow-up clinics in three Canadian provinces was undertaken. RESULTS: De novo PTDM was diagnosed in 184/905 (20.3%) of liver and 179/390 (45.9%) of renal transplant recipients. Older age, higher pre-transplant BMI, underlying immune-mediated liver disease, lower trough tacrolimus levels and longer duration of follow-up were independently associated with PTDM in liver transplant recipients and non-Caucasian race, higher pre-transplant body mass index (BMI), and incidence of organ rejection in renal transplant recipients. Compared with Caucasians, Indigenous Canadians who had undergone renal transplantation had a significantly increased prevalence of PTDM (56.5% vs 40.0%, p = 0.035). The prevalence of PTDM in liver transplant recipients was similar in Indigenous Canadians and Caucasians (27.9% vs 20.1%, p = 0.215). CONCLUSIONS: The variables associated with PTDM differ in liver and renal transplant recipients. Compared with Caucasians, Indigenous Canadians undergoing renal transplantation are at increased risk of developing PTDM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".