Exercise interventions in solid organ transplant candidates: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Exercise training may be recommended to solid organ transplant (SOT) candidates to improve fitness and tolerance before surgery. We aimed to determine the acceptance, safety, and effectiveness of exercise interventions in SOT candidates. METHODS: Online databases were searched. Studies of any design were included. Outcomes of interest were acceptance, safety, exercise capacity, and health-related quality of life. RESULTS: Twenty-three articles were included. Acceptance ranged from 16% to 100%. In the fifteen studies that assessed adverse events, none mentioned any adverse events occurring during the study. Five out of seven studies reported an increase in maximal exercise capacity post-exercise in the intervention group (range of mean change: 0.45 to 2.9 mL/kg). Eight out of fourteen studies reported an increase in 6-minute walking distance in the intervention group after the training period (range of mean change: 40-105 m). Two articles showed an improvement in the mental composite scores as well as in the physical composite scores post-exercise in the intervention group. CONCLUSION: There was a lack of significant findings among most randomized controlled trials. Exercise training is acceptable and safe for selective SOT candidates. The effects of exercise training on exercise capacity and quality of life in SOT candidates are unclear.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".