Predictors of early and long-term readmissions and their association with survival after liver transplantation
Bibliographic record
Abstract
Background: The impact of post liver transplantation (LT) readmissions on mortality has not been well described. Thus, the primary objective of our study was to determine predictors of readmissions post-LT and assess impact on survival. Methods: Single center retrospective observational study investigating adult patients who underwent LT between January 1, 2010 – December 31, 2019 at Toronto General Hospital (TGH). Time-dependent cox regression model was used to investigate risk factors for 30-day, 30–90-day, and >90-day readmissions to hospital. The effect of readmission on survival was assessed with the Kaplan–Meier estimator. Results: 987 patients fulfilled inclusion criteria. Significant predictors of 30-day readmissions were BMI > 30 kg/m2 (HR=0.64; CI 0.42–0.98; p-value 0.04) and autoimmune/cholestatic liver disease (HR=1.86; CI 1.01–3.42; p = 0.046) at 30-days. Post-LT length of stay (HR=1.05; CI 1.02–1.08; p<0.001) at 30–90 days. Meanwhile, living donor LT (HR=1.41; CI 1.06–1.89; p = 0.02) and distance from LT center (HR=1.05; CI 1.01–1.09; p = 0.011) after 90 days. Infection was the main reason for readmission across three time periods. An inpatient readmission across any time period was found to be significantly associated with mortality (HR=2.4; 1.6–3.6; p<0.0001). Conclusion: Hospital readmissions post-LT are associated with increased mortality. Although infection is a common risk factor for readmission other modifiable risk factors may be an area for target of interventions to reduce post-LT readmission.
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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".