Enhancing postoperative recovery with multimodal prehabilitation: the journey begins before surgery
Bibliographic record
Abstract
This narrative review explores multimodal prehabilitation, a patient-centered, evidence-based, and multidisciplinary approach to enhance postoperative recovery. It shifts the focus from traditional intraoperative and postoperative care to a comprehensive process beginning at diagnosis. Multimodal prehabilitation integrates exercise, nutrition, and psychological strategies to improve preoperative functional capacity and physiological reserve, enabling better management of surgical stress. The review examines prehabilitation's clinical efficacy, highlighting enhanced functional capacity as a key outcome. It details prehabilitation components: exercise (aerobic, resistance, and respiratory muscle training), nutritional optimization targeting modifiable risk factors such as malnutrition and sarcopenia, and psychological support to lower anxiety and boost patient motivation and adherence. Individualized approaches are emphasized due to significant patient variability. This review also presents a successful multimodal prehabilitation program implemented at the Montreal General Hospital, which has a strong track record in this area. The program is structured around four key phases: screening, assessment, intervention, and follow-up. It also discusses the barriers to implementation and the roles of stakeholders, including the government, hospitals, healthcare professionals, and patients and their families, within the context of South Korea's unique healthcare system and socio-cultural environment.
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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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".