Prospective assessment of the impact of intraoperative diuretics in kidney transplant recipient surgery
Bibliographic record
Abstract
Background: The use of intraoperative diuretics, such as furosemide or mannitol, during kidney transplantation has been suggested to reduce the rate of delayed graft function (DGF). The evidence base for this is sparse, however, and there is substantial variation in practice. We sought to evaluate whether the use of intraoperative diuretics during kidney transplantation translated into a reduction in DGF. Methods: We conducted a cohort study evaluating the use of furosemide or mannitol given intraoperatively before kidney reperfusion compared with control (no diuretic). Adult patients receiving a kidney transplant for end-stage renal disease were allocated to receive furosemide, mannitol, or no diuretic. The primary outcome was DGF; secondary outcomes were graft function at 30 days and perioperative changes in potassium levels. Descriptive and comparative statistics were used where appropriate. Results: A total of 162 patients who received a kidney transplant from a deceased donor (either donation after neurologic determination of death or donation after circulatory death) were included over a 2-year period, with no significant between-group differences. There was no significant difference in DGF rates between the furosemide, mannitol, and control groups. When the furosemide and mannitol groups were pooled (any diuretic use) and compared with the control group, however, there was a significant improvement in the odds that patients would be free of DGF (odds ratio 2.10, 95% confidence interval 1.06–4.16, 26% v. 44%, p = 0.03). There were no significant differences noted in any secondary outcomes. Conclusion: This study suggests the use of an intraoperative diuretic (furosemide or mannitol) may result in a reduction in DGF in patients undergoing kidney transplantation. Further study in the form of a randomized controlled trial is warranted.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".