Does intra‐operative verapamil administration in kidney transplantation improve graft function
Bibliographic record
Abstract
The role of the calcium channel blocker (verapamil) in kidney transplant is controversial. Verapamil has been hypothesized to mitigate ischemia reperfusion injury (IRI) to the allograft. Herein, we evaluated the effect of intra-operative verapamil administration in a large cohort of kidney transplants. Total 684 transplants were performed during 2007-2017. Of these, 348 (50.9%) transplants received verapamil (2.5 mg) Ver (+), and 336 (49.1%) did not, Ver (-). Based on the donor type, the study was divided into three groups; living donor (LD) (N = 270), neurological determination of death (NDD) (N = 394), and donation after cardiac death (DCD) (N = 20). Ver (-) subgroup had more diabetic recipients as compared to Ver (+) subgroup in LD and NDD groups (P < 0.05). No significant difference was found for delayed graft function in any of the group (P > 0.05). Cold ischemia time and dialysis requirement were significantly higher in Ver (+) LD and NDD groups, respectively. Except for DCD group, there was no significant difference in eGFR (mL/min) immediately and 6 months after kidney transplant in any of the groups. Furthermore, univariate and multivariate logistic regression analysis was performed to account for potential confounders, but verapamil administration did not improve graft function in any of the groups (P > 0.05) after transplant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".