The impact of infections on delisting patients from the liver transplantation waiting list
Bibliographic record
Abstract
Approximately 20% of the patients listed for liver transplantation die before transplantation can be accomplished. Understanding risk factors for waiting list mortality may help to improve survival and organ allocation. Infections are very common in patients with cirrhosis and are associated with significant morbidity and mortality. This study analysed the frequency and characteristics of infections in patients awaiting liver transplantation, identified risk factors for withdrawal from the waiting list and evaluated the impact of infections on the clinical outcome. A retrospective analysis of consecutive patients listed for liver transplantation in Rotterdam, the Netherlands from 2007 to 2014 was conducted. Infections occurred in 144 of 327 studied patients (44%). In this cohort, 23.4% of the patients on the liver transplantation waiting list were delisted or died before transplantation. Patients with an infection were 5.2 times more likely to become delisted than noninfected patients. In the 30 days after the first infection, patients were 33.8 times more likely to become delisted compared to noninfected patients. High age, high MELD score, refractory ascites and inappropriate antibiotic therapy were independent predictors for delisting due to infection. Infections occur frequently in patients on the liver transplantation waiting list. Emphasis on appropriate and timely antimicrobial therapy is 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".