The role of prehabilitation on short‐term outcomes after liver transplantation: A review of the literature and expert panel recommendations
Bibliographic record
Abstract
BACKGROUND: Prehabilitation programs as part of ERAS protocols are being increasingly used in multiple surgeries, improving postoperative outcomes. Data regarding prehabilitation programs in patients awaiting liver transplantation and their outcomes is scarce. OBJECTIVES: To identify whether prehabilitation programs based on exercise training conducted prior to liver transplantation improve short-term postoperative outcomes, and to provide expert panel recommendations. DATA SOURCES: Ovid MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Central. METHODS: Systematic review following PRISMA guidelines and recommendations using the GRADE approach derived from an international expert panel. Studies included those evaluating postoperative outcomes, as well as those evaluating functional outcomes. PROSPERO ID: CRD42021236305. RESULTS: Of the 170 studies screened, only one assessed the primary objective. Most studies focus on the preoperative impact of exercise training on aerobic capacity, muscle mass and/or strength, showing positive effects and no significant adverse events, but are underpowered and with heterogenous designs and interventions. The non-randomized observational study which assessed relevant postoperative outcomes, showed a non-significant trend towards reduced 90-day readmission rate and shorter length of stay in the prehabilitation group. CONCLUSIONS: Prehabilitation prior to liver transplantation is unlikely to be harmful, and likely to have short term benefits on functional status. We cautiously recommend prehabilitation on the basis of absence of harm and possibility of benefit (Quality of Evidence; Very Low | Grade of Recommendation; Low).
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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 | 0.064 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".