Low‐dose aspirin confers protection against acute cellular allograft rejection after primary liver transplantation
Bibliographic record
Abstract
This study investigated the effect of low-dose aspirin in primary adult liver transplantation (LT) on acute cellular rejection (ACR) as well as arterial patency rates. The use of low-dose aspirin after LT is practiced by many transplant centers to minimize the risk of hepatic artery thrombosis (HAT), although solid recommendations do not exist. However, aspirin also possesses potent anti-inflammatory properties and might mitigate inflammatory processes after LT, such as rejection. Therefore, we hypothesized that the use of aspirin after LT has a protective effect against ACR. This is an international, multicenter cohort study of primary adult deceased donor LT. The study included 17 high-volume LT centers and covered the 3-year period from 2013 to 2015 to allow a minimum 5-year follow-up. In this cohort of 2365 patients, prophylactic antiplatelet therapy with low-dose aspirin was administered in 1436 recipients (61%). The 1-year rejection-free survival rate was 89% in the aspirin group versus 82% in the no-aspirin group (hazard ratio [HR], 0.77; 95% confidence interval [CI], 0.63-0.94; p = 0.01). The 1-year primary arterial patency rates were 99% in the aspirin group and 96% in the no-aspirin group with an HR of 0.23 (95% CI, 0.13-0.40; p < 0.001). Low-dose aspirin was associated with a lower risk of ACR and HAT after LT, especially in the first vulnerable year after transplantation. Therefore, low-dose aspirin use after primary LT should be evaluated to protect the liver graft from ACR and to maintain arterial patency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".