Poster Session I (Abstracts 259 – 729)
Bibliographic record
Abstract
AASLD Presidential Poster of DistinctionBackground Acetaminophen is the leading cause of drug-induced liver injury (DILI) and constitutes majority of DILI-related liver transplants (LT) in United States (US) .We evaluated survival following liver transplantation in patients with acetaminophen-related DILI .Methods We conducted a cohort study using US national data from the United Network for Organ Sharing (UNOS) registry to evaluate temporal trends and 1-year post-LT survival in patients with acetaminophen-related DILI from 2000-2014 .Demographic and clinical data were evaluated in two acetaminophen-DILI cohorts 1) candidates transplanted from 2000-2006 and 2) candidates transplanted from 2007-2013 .Survival analysis was conducted using Kaplan-Meir methods . ResultsOverall from 2000 to 2014 a total of 553 DILI-related LT were performed, of which acetaminophen-induced DILI resulted in 254 (46%) LT .Although there was no discernable annual trend in LT associated with acetaminophen-related DILI, the number of cases with DILI secondary to acetaminophen peaked in 2007 and lead to 37 LT .When comparing two eras -2000When comparing two eras - -2006When comparing two eras - versus 2007When comparing two eras - -2013, the percentage of LT due to acetaminophen-related DILI increased from 43 .5% to 52 .4% .1-year post-LT survival was markedly improved (Figure ) from 2000-2006 to 2007-2013 from 67 .5% to 79 .3%(p-value = 0 .05) .Liver transplant recipients from 2007-2013 were slightly older (34 .7 to 33 .7)with higher prevalence of female gender (58 .2% to 23 .5%)and Caucasian ethnicity (59 .2% to 22 .5%) .Conclusion Our data demonstrates changes in demographics, incremental increase in the rate of LT, and a marked improvement in the short-term survival following liver transplantation in patients with acetaminophen-induced DILI in recent years .
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.459 | 0.203 |
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".