Perioperative fluid management in kidney transplantation: a black box
Bibliographic record
Abstract
The incidence of delayed graft function in patients undergoing kidney transplantation remains significant. Optimal fluid therapy has been shown to decrease delayed graft function after renal transplantation. Traditionally, the perioperative volume infusion regimen in this patient population has been guided by central venous pressure as an estimation of the patient's volume status and mean arterial pressure, but this is based on sparse evidence from mostly retrospective observational studies. Excessive volume infusion to the point of no further fluid responsiveness can damage the endothelial glycocalyx and is no longer considered to be the best approach. However, achievement of adequate flow to maintain sufficient tissue perfusion without maximization of cardiac filling remains a challenge. Novel minimally invasive technologies seem to reliably assess volume responsiveness, heart function and perfusion adequacy. Prospective comparative clinical studies are required to better understand the use of dynamic analyses of flow parameters for adequate fluid management in kidney transplant recipients. We review perioperative fluid assessment techniques and discuss conventional and novel monitoring strategies in the kidney transplant recipient.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".