Prehabilitation in surgery – an update with a focus on nutrition
Bibliographic record
Abstract
PURPOSE OF REVIEW: Since the introduction of the prehabilitation concept for optimizing functional capacity before surgery 20 years ago, evidence and interest has grown considerably. This review summarizes the recent evidence and proposes questions for prehabilitation with special regard to the nutritional component. RECENT FINDINGS: Several meta-analyses of multimodal prehabilitation (exercise, nutrition, and psychological support) have been published recently. These reviews suggest that preoperative conditioning can improve functional capacity and reduce the complication rate for many patient groups (risk of bias: moderate to low). A prerequisite is the identification of high-risk patients using suitable screening and assessment tools. Additionally, there are currently no standardized, clear recommendations for the organization and implementation of prehabilitation programs. The programs vary greatly in duration, content, and outcome measurement. Although the preoperative nutrition interventions enhanced outcomes consistently, there was no clear evidence for which nutritional intervention should be applied to whom over consistent time frame four to six weeks (timeframe consistent with most prehabilitation programs). SUMMARY: To advance our understanding of which prehabilitation interventions work best, how they work, and for whom they work best, additional low risk of bias and adequately powered trials are required. Nevertheless, our review presents evidence that prehabilitation should be offered before major surgery on a risk-stratified basis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".