New Onset Diabetes Mellitus in Patients Receiving Calcineurin Inhibitors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
New onset diabetes mellitus (NODM) is a serious complication of transplantation. This meta-analysis evaluates the reported incidence of NODM after solid organ transplantation in patients receiving CNI treatment. Databases from January 1992 to April 2002 were searched. Fifty-six publications providing NODM incidence data were reviewed. Sixteen prospective, randomized comparative studies providing information on incidence of insulin-dependent diabetes mellitus (IDDM) were subjected to meta-analysis. New onset diabetes mellitus was reported in 13.4% of patients after solid organ transplantation, with a higher incidence in patients receiving tacrolimus than cyclosporine (16.6% vs. 9.8%). This trend was observed across renal, liver, heart and lung transplant groups. Meta-analysis of 16 studies included patients receiving either tacrolimus (n=1636) or cyclosporine (n=1407). The incidence of IDDM was significantly higher among tacrolimus-treated patients (10.4% vs. 4.5%, p<0.00001), an effect observed in renal (9.8% vs. 2.7% p<0.00001) and nonrenal (11.1% vs. 6.2%; p<0.003) groups, and among patients receiving equal doses of concomitant medication in both treatment arms (12.0% vs. 3.0%; p<0.00001). The reported incidence of NODM during the past decade was significantly higher among patients receiving tacrolimus than cyclosporine. These data provide a quantitative foundation for studies designed to reduce the rates of NODM following solid organ transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".