The effect of goal-directed fluid therapy on delayed graft function in kidney transplant recipients: A systematic review and meta-analysis
Bibliographic record
Abstract
Delayed graft function (DGF) is a common post-operative complication with potential long-term sequelae for many kidney transplant recipients, and hemodynamic factors and fluid status play a role. Fixed perioperative fluid infusions are the standard of care, but more recent evidence in the non-transplant population has suggested benefit with goal-directed fluid strategies based on hemodynamic targets. We searched MEDLINE, EMBASE, Cochrane Controlled Trials Registry and Google Scholar through December 2022 for randomized controlled trials comparing risk of DGF between goal-directed and conventional fluid therapy in adults receiving a living or deceased donor kidney transplant. Effect estimates were reported with odds ratios (OR) and pooled using random effects meta-analysis. We identified 4 studies (205 participants) that met the inclusion criteria. The use of goal-directed fluid therapy had no significant effect on DGF (OR 1.37 95% CI, 0.34–5.6; p = 0.52; I2 = 0.11). Subgroup analysis examining effects among deceased and living kidney donation did not reveal significant differences in the effects of fluid strategy on DGF between subgroups. Overall, the strength of the evidence for goal-directed versus conventional fluid therapy to reduce DGF was of low certainty. Our findings highlight the need for larger trials to determine the effect of goal-directed fluid therapy on this patient-centered outcome.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".