High risk of delisting or death in liver transplant candidates following infections: Results from the North American consortium for the study of end‐stage liver disease
Bibliographic record
Abstract
Because Model for End-Stage Liver Disease (MELD) scores at the time of liver transplantation (LT) increase nationwide, patients are at an increased risk for delisting by becoming too sick or dying while awaiting transplantation. We quantified the risk and defined the predictors of delisting or death in patients with cirrhosis hospitalized with an infection. North American Consortium for the Study of End-Stage Liver Disease (NACSELD) is a 15-center consortium of tertiary-care hepatology centers that prospectively enroll and collect data on infected patients with cirrhosis. Of the 413 patients evaluated, 136 were listed for LT. The listed patients' median age was 55.18 years, 58% were male, and 47% were hepatitis C virus infected, with a mean MELD score of 2303. At 6-month follow-up, 42% (57/136) of patients were delisted/died, 35% (47/136) underwent transplantation, and 24% (32/136) remained listed for transplant. The frequency and types of infection were similar among all 3 groups. MELD scores were highest in those who were delisted/died and were lowest in those remaining listed (25.07, 24.26, 17.59, respectively; P < 0.001). Those who were delisted or died, rather than those who underwent transplantation or were awaiting transplantation, had the highest proportion of 3 or 4 organ failures at hospitalization versus those transplanted or those continuing to await LT (38%, 11%, and 3%, respectively; P = 0.004). For those who were delisted or died, underwent transplantation, or were awaiting transplantation, organ failures were dominated by respiratory (41%, 17%, and 3%, respectively; P < 0.001) and circulatory failures (42%, 16%, and 3%, respectively; P < 0.001). LT-listed patients with end-stage liver disease and infection have a 42% risk of delisting/death within a 6-month period following an admission. The number of organ failures was highly predictive of the risk for delisting/death. Strategies focusing on prevention of infections and extrahepatic organ failure in listed patients with cirrhosis are required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".