Efficacy of Corticosteroid Pretreatment in the Management of Deceased Organ Donors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Corticosteroid pretreatment of deceased donors may improve transplant outcomes by counteracting hypothalamic-pituitary-adrenal axis dysfunction and systemic inflammation, but the evidence from randomized controlled trials (RCTs) remains inconclusive. This systematic review aims to elucidate any potential transplant outcome benefits through a meta-analysis. METHODS: A systematic review and meta-analyses were conducted following PRISMA 2020 guidelines. RCTs examining corticosteroid administration to deceased donors were identified through database searches in Ovid MEDLINE, Ovid Embase, CENTRAL, and Web of Science (Core Collection), up to March 2024. Random-effects meta-analysis and subgroup analyses were performed, with evidence quality assessed using GRADE. RESULTS: Eleven studies examining 10 RCTs (n = 687 donors; 1680 recipients) were included. Corticosteroid pretreatment showed no significant benefit for incidence of donor vasopressor use (RR 0.95, 95% CI 0.87 to 1.05), delayed recipient graft function (RR 0.91, 95% CI 0.53-1.57), recipient graft dysfunction (RR 0.95, 95% CI 0.83-1.10), recipient length of stay (MD 0.46 days, 95% CI -6.99-7.91), recipient acute graft rejection (RR 0.93, 95% CI 0.53-1.61), recipient graft survival (RR 1.03, 95% CI 0.98-1.10), and recipient mortality (RR 0.72, 95% CI 0.37-1.38). Subgroup and meta-regression analyses revealed no consistent benefits, though heterogeneity in corticosteroid regimens and donor-recipient characteristics limited interpretation. The risk of bias was high across most studies. CONCLUSIONS: Corticosteroid pretreatment demonstrates no clear benefit for deceased donor transplant outcomes. Methodological limitations and heterogeneity underscore the need for robust RCTs.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.005 | 0.005 |
| 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".