Heparin infusion in simultaneous pancreas and kidney transplantation reduces graft thrombosis and improves graft survival
Bibliographic record
Abstract
INTRODUCTION: Thrombosis of the pancreas after transplantation is the most common cause of relaparotomy and resultant graft loss. There is currently no standard protocol consistently proven to prevent thrombosis following transplantation. Our objective was to determine whether our protocol of post-operative low-dose intravenous (IV) heparin infusion would prevent graft thrombosis without additional complications in our patients. METHODS: A total of 66 simultaneous pancreas kidney (SPK) transplants were performed at our institution from 2004 to 2014. Patients were divided into 2 retrospective cohort groups. Group 1 patients received only acetylsalicylic acid (ASA) 81 mg/d started on post-operative day 1. Group 2 patients received IV heparin infusion beginning in the recovery room at a rate of 500 IU/h for the first 24 hours, reduced by 100 IU/h every day to stop on day 5, and then received ASA 81 mg/d afterward. Outcome and complication rates were compared between the two groups for 5 years post-transplant. RESULTS: We observed a significant reduction in graft thrombosis and graft loss with (0/29) patients in the heparin group vs (7/33) 25.7% from the non-heparin (P<.01) with no differences in complication rates. CONCLUSIONS: We present a heparin infusion protocol which may help prevent graft thrombosis and graft loss in SPK transplantation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".