Effect of corticosteroid administration on neurologically deceased organ donors and transplant recipients: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This review investigates the impact of corticosteroids on donation rates and transplant outcomes in light of findings from randomised controlled trials (RCTs) and to highlight the sources of uncertainty in this unresolved donor management issue. DATA SOURCES: We searched electronic databases, trial registries and conference proceedings for RCTs evaluating corticosteroid therapy in neurologically deceased donors. STUDY SELECTION AND DATA EXTRACTION: Independent reviewers assessed eligibility, evaluated risk of bias and abstracted data, including donor haemodynamic data, number of organs recovered and transplant outcomes. Where possible, we pooled results. For each outcome, we assessed the overall quality of evidence using The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. DATA SYNTHESIS: Eleven RCTs with different corticosteroid regimens were included. Most trials assessed a once-daily infusion of methylprednisolone. Aside from one study showing improved liver graft function, no individual study or pooled analysis showed benefit of corticosteroids for any outcome: vasopressor use (three trials; relative risk (RR) 0.96; 95% CI 0.89 to 1.05), multiple organs recovered (two trials; RR 0.82; 95% CI 0.61 to 1.11), acute graft rejection (three trials; RR 0.91; 95% CI 0.60 to 1.39) or graft dysfunction (eight trials; RR 1.01; 95% CI 0.83 to 1.24). Two trials investigated adverse effects and found similar rates between groups. Quality of evidence was moderate or low for all outcomes. CONCLUSION: Current clinical trials are limited in numbers and size to identify benefits or harms of corticosteroid therapy for deceased organ donors. In the face of these results, administering or withholding steroids both appear reasonable courses of action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".