Heparin Thromboprophylaxis in Simultaneous Pancreas-Kidney Transplantation: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Thrombosis is a leading causes of pancreas graft loss after simultaneous pancreas kidney (SPK), pancreas after kidney (PAK), and pancreas transplant alone (PTA). There remains no standardized thromboprophylaxis protocol. The aim of this systematic review and meta-analysis is to evaluate the impact of heparin thromboprophylaxis on the incidence of pancreas thrombosis, pancreas graft loss, bleeding, and secondary outcomes in SPK, PAK, and PTA. Following PRISMA guidelines, we systematically searched BIOSIS®, PubMed®, Cochrane Library®, EMBASE®, MEDLINE®, and Web of Science® on April 21, 2021. Primary peer-reviewed studies that met inclusion criteria were included. Two methods of quantitative synthesis were performed to account for comparative and non-comparative studies. We included 11 studies, comprising of 1,122 patients in the heparin group and 236 patients in the no-heparin group. When compared to the no-heparin control, prophylactic heparinization significantly decreased the risk of early pancreas thrombosis and pancreas loss for SPK, PAK and PTA without increasing the incidence of bleeding or acute return to the operating room. Heparin thromboprophylaxis yields an approximate two-fold reduction in both pancreas thrombosis and pancreas loss for SPK, PAK and PTA. We report the dosage, frequency, and duration of heparin administration to consolidate the available evidence.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".