Multimodal Prehabilitation Programs as a Bundle of Care in Gastrointestinal Cancer Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Prehabilitation reflects a proactive process of preoperative optimization undertaken between cancer diagnosis and definitive surgical treatment, with the intent of improving physiological capacity to withstand the major insult of surgery. Prehabilitation before GI cancer surgery is currently not widely adopted, and most research has focused on unimodal interventions such as exercise therapy, nutritional supplementation, and hematinic optimization. A review of the existing literature was undertaken to investigate the impact of multimodal prehabilitation programs as a "bundle of care." DATA SOURCE: A systematic literature search was performed utilizing Medline, PubMed, Embase, Cinahl, Cochrane, and Google Scholar databases. STUDY SELECTION: The quality of studies was assessed by using the Cochrane tool for assessing risk of bias (randomized trials) and the Newcastle-Ottawa Quality Assessment scale (cohort studies). INTERVENTION: Studies were chosen that involved pre-operative optimization of patients before GI cancer surgery. MAIN OUTCOMES: The primary outcome measured was the impact of prehabilitation programs on preoperative fitness and postoperative outcomes. RESULTS: Of the 544 studies identified, 20 were included in the qualitative analysis. Two trials investigated the impact of multimodal prehabilitation (exercise, nutritional supplementation, anxiety management). Trials exploring prehabilitation with unimodal interventions included impact of exercise therapy (7 trials), impact of preoperative iron replacement (5 trials), nutritional optimization (5 trials), and impact of preoperative smoking cessation (2 trials). Compliance within the identified studies was variable (range: 16%-100%). LIMITATIONS: There is a lack of adequately powered trials that utilize objective risk stratification and uniform end points. As such, a meta-analysis was not performed because of the heterogeneity in study design. CONCLUSION: Although small studies are supportive of multimodal interventions, there are insufficient data to make a conclusion about the integration of prehabilitation in GI cancer surgery as a bundle of care. Larger, prospective trials, utilizing uniform objective risk stratification and structured interventions, with predefined clinical and health economic end points, are required before definitive value can be assigned to prehabilitation programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".