When is the optimal time to discharge patients after liver transplantation with respect to short‐term outcomes? A systematic review of the literature and expert panel recommendations
Bibliographic record
Abstract
BACKGROUND: Several factors associated with prolonged hospital stay have been described. A recent study demonstrated that hospital length of stay (LOS) is directly associated with an increased cost for liver transplantation (LT) and may be associated with greater mortality; however, the factors associated with post-LT mortality are also related to a prolonged hospital stay, that is, those factors are confounders. Thus, the actual impact of the length of post-LT hospital stay on both short-term and long-term patient and graft survival remains uncertain. OBJECTIVES: To identify the optimal time to discharge patients after LT with respect to short-term outcomes; readmission rate, 30-90-mortality and morbidity. METHODS: Systematic review following PRISMA guidelines and recommendations using the GRADE approach derived from an international expert panel. Initial search keywords for screening were as follows; ((discharge AND (time OR "time point" OR "time-point")) OR "length of hospital stay" OR "length of stay") AND ((liver OR hepatic) AND (transplant OR transplantation)). PROSPERO ID: CRD42021245598 RESULTS: The strength of recommendation was rated as Weak, and we did not identify the direction of recommendations regarding the optimal timing after LT concerning short-term outcomes, including "Readmission rate," six studies on 30- and/or 90-day mortality, and five studies on "30- and/or 90-day morbidity rate." CONCLUSIONS: Evidence is scarce to judge the optimal timing to discharge patients after LT with respect to short-term outcomes. In centers with robust outpatient follow-up, discharge can occur safely as early as post-transplant 6-8 days (Quality of Evidence [QOE]; Low | Grade of Recommendation; Weak).
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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 | 0.065 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".