Calcineurin Inhibition in Deceased Organ Donors: A Systematic Review and Meta-analysis of Preclinical Studies
Bibliographic record
Abstract
Background: Preconditioning deceased organ donors with calcineurin inhibitors (CNIs) may reduce ischemia-reperfusion injury to improve transplant outcomes. Methods: We searched MEDLINE, EMBASE, Cochrane Library, and conference proceedings for animal models of organ donation and transplantation, comparing donor treatment with CNIs with either placebo or no intervention, and evaluating outcomes for organ transplantation. Reviewers independently screened and selected studies, abstracted data, and assessed the risk of bias and clinical relevance of included studies. Where possible, we pooled results using meta-analysis; otherwise, we summarized findings descriptively. Results: Eighteen studies used various animals and a range of CNI agents and doses and evaluated their effects on a variety of transplant outcomes. The risk of bias and clinical applicability were poorly reported. Pooled analyses suggested benefit of CNI treatment on early graft function in renal transplants (3 studies; serum creatinine: ratio of means [RoM] 0.54; 95% confidence interval [CI], 0.34-0.86) but not for liver transplants (2 studies; serum alanine transaminase: RoM 0.61; 95% CI, 0.30-1.26; and serum aspartate aminotransferase: RoM 0.58; 95% CI, 0.26-1.31). We found no reduction in graft loss at 7 d (2 studies; risk ratio 0.54; 95% CI, 0.08-3.42). CNI treatment was associated with reduced transplant recipient levels of interleukin-6 (4 studies; RoM 0.36; 95% CI, 0.19-0.70), tumor necrosis factor-alpha (5 studies; RoM 0.36; 95% CI, 0.12-1.03), and cellular apoptosis (4 studies; RoM 0.30; 95% CI, 0.19-0.47). Conclusions: Although this compendium of animal experiments suggests that donor preconditioning with CNIs may improve early kidney graft function, the limited ability to reproduce a true clinical environment in animal experiments and to assess for risk of bias in these experiments is a serious weakness that precludes current clinical application.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".