Enhancing Health Outcomes through Optimized Body Composition in Prehabilitation
Bibliographic record
Abstract
BACKGROUND: Prehabilitation, the process of optimizing a patient's physical and nutritional status before surgery, has gained increasing attention for its potential to improve outcomes by enhancing physiological reserves and functional capacity. While body composition may play a role in these outcomes, its specific contribution remains underexplored. This narrative review summarizes current evidence on the effects of prehabilitation on body composition, focusing on exercise, nutritional interventions, and multimodal approaches. SUMMARY: Exercise, particularly a combination of aerobic and resistance training, has been shown to improve cardiorespiratory fitness, reduce fat mass, and enhance skeletal muscle and strength. Nutritional interventions, including increased protein intake, support skeletal muscle preservation, and recovery. A multimodal approach, integrating both exercise and nutrition, yields the most significant improvements in body composition, showing enhanced skeletal muscle, reduced fat mass, and better functional outcomes. However, the limited duration of prehabilitation and the time required for detectable changes in body composition often prevent consistent observations. Furthermore, variations in assessment techniques and protocols across studies confound definitive conclusions. KEY MESSAGES: Despite some promising results, further research is needed to standardize protocols and explore the effects of prehabilitation on body composition across diverse patient populations. Finally, further research is needed to investigate the impact of prehabilitation on measurable changes in body composition as this represents a critical gap in the field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".