Impact of immunosuppression on incidence of post-transplant diabetes mellitus in solid organ transplant recipients: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Solid organ transplantation is a life-saving intervention for end-stage organ disease. Post-transplant diabetes mellitus (PTDM) is a common complication in solid organ transplant recipients, and significantly compromises long-term survival beyond a year. AIM: To perform a systematic review and meta-analysis to estimate incidence of PTDM and compare the effects of the 3 major immunosuppressants on incidence of PTDM. METHODS: Two hundred and six eligible studies identified 75595 patients on Tacrolimus, 51242 on Cyclosporine and 3020 on Sirolimus. Random effects meta-analyses was used to calculate incidence. RESULTS: Network meta-analysis estimated the overall risk of developing PTDM was higher with tacrolimus (OR = 1.4 95%CI: 1.0-2.0) and sirolimus (OR = 1.8; 95%CI: 1.5-2.2) than with Cyclosporine. The overall incidence of PTDM at years 2-3 was 17% for kidney, 19% for liver and 22% for heart. The risk factors for PTDM most frequently identified in the primary studies were age, body mass index, hepatitis C, and African American descent. CONCLUSION: Tacrolimus tends to exhibit higher diabetogenicity in the short-term (2-3 years post-transplant), whereas sirolimus exhibits higher diabetogenicity in the long-term (5-10 years post-transplant). This study will aid clinicians in recognition of risk factors for PTDM and encourage careful evaluation of the risk/benefit of different immunosuppressant regimens in transplant recipients.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".