Defining readmission risk factors for liver transplantation recipients.
Bibliographic record
Abstract
Liver transplantation (LT) is a costly but effective treatment for end-stage liver disease (ESLD). However, there are minimal data on the patterns of and risk factors for hospital readmission after LT. The aim of this study was to determine the frequency of and risk factors for rehospitalization after LT. Consecutive adult patients who underwent LT at a single center (n = 208) were prospectively studied over a 30-month period. Within 90 days of LT, 30.3% of LT recipients were readmitted to the hospital. Recipient and donor age, Model for End-Stage Liver Disease score, cold ischemia time, type of hepatic graft, length of hospitalization after LT, and occurrence of operative/postoperative complications had no association with the risk for readmission (P>.05). The length of stay in intensive care was negatively correlated with readmission (hazard ratio, 0.92; P=.028). ESLD from hepatitis C virus (HCV) infection as an indication for LT was the only factor associated with an increased risk for readmission (hazard ratio, 1.91 ; P=.010). Further studies are needed to explore the reasons for readmission among LT recipients, particularly those with HCV infection, in order to devise cost-savings policies for post-LT care.
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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 | 0.005 |
| 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 | 0.001 |
| 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".