Efficacy of cold renal perfusion protection for open complex aortic aneurysm repair: a meta-analysis
Bibliographic record
Abstract
Background: Cold renal perfusion (CRP) with 4°C crystalloid fluids has been described as a method to reduce renal injury during open surgical repair of complex aortic aneurysms (cAAs) (those requiring at least a suprarenal clamp site). We performed a meta-analysis to ascertain whether CRP improves kidney-related outcomes after open surgical cAA repair. Methods: Patients of any age or gender who had undergone open surgical repair of cAAs were included. Primary outcomes were the presence of postoperative kidney injury, the need for dialysis and mortality related to kidney injury. We compared patients who were treated with any intraoperative CRP strategy to a control population without CRP. We used a fixed-effects model to analyze derived odds ratios (ORs) and assess heterogeneity. We performed risk of bias analysis to identify potential confounding elements. Results: Among the 935 studies screened, 5 primary articles met the inclusion criteria. Cold renal perfusion significantly reduced postoperative acute kidney injury (OR 0.46 [95% confidence interval 0.32–0.68], Z = 3.98, p = 0.001). Only 1 study included data for the other primary outcomes. The data were considered homogeneous, with Cochrane Q = 0.23 and I2 of 0%. Conclusion: This meta-analysis showed reduced postoperative acute kidney injury with the use of CRP during open cAA repair. A prospective randomized controlled trial to perform further subgroup analysis and research the various types of CRP solutions may be warranted to identify further possible benefits.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".