Prehabilitation in Adult Solid Organ Transplant Candidates
Bibliographic record
Abstract
Purpose of Review: To highlight the importance of biological age in the context of prehabilitation and to present relevant research regarding prehabilitation prior to solid organ transplantation. Recent Findings: Studies on the effect of prehabilitation have been performed in kidney-, lung-, liver-, and heart transplant patient populations. Prior to kidney transplantation, exercise interventions have been shown to improve cardiopulmonary- and physical fitness and result in a decreased length of hospital stay postoperatively. Among lung transplant candidates, various methods of prehabilitation have been studied including home-based, outpatient and in-patient programs, consisting of physical training, psychological support, education, and nutritional interventions. Overall, prehabilitation seems to improve or maintain quality of life and exercise capacity in this patient population. Patients undergoing liver transplantation seem to benefit from prehabilitation as well. Not only does it seem safe and feasible, but significant improvements in aerobic and functional capacity have also been found. Regarding heart transplant candidates, both inpatient and outpatient, supervised prehabilitation programs show promising results with improvements in exercise capacities and quality of life. Summary: Prehabilitation is an effective and safe intervention for improving functional outcomes of solid organ transplant patients. Future studies should evaluate whether prehabilitation translates into improved pre- and post-transplant clinical outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".