Cancer prehabilitation implementation: systematic review protocols on guidelines, implementation barriers and strategies
Bibliographic record
Abstract
Cancer prehabilitation consists of targeted interventions initiated after diagnosis and before treatment to enhance patients’ capacity to tolerate treatment stress, improve postoperative outcomes, and accelerate recovery. It may be delivered as a unimodal intervention—exercise, nutrition, cognitive, or psychosocial—or more commonly as a multimodal approach integrating two or more components. As cancer surgeries rise globally, prehabilitation has become a priority for patients, clinicians, and health systems. Evidence shows that exercise, nutrition, and multicomponent interventions improve recovery, reduce complications, shorten hospital stays, and enhance quality of life. Despite strong evidence, prehabilitation remains poorly implemented. Barriers exist at patient, clinician, and system levels, including limited awareness, lack of knowledge, resource shortages, and inadequate reimbursement. Facilitators such as patient motivation and caregiver support highlight opportunities to improve uptake. First, most existing research comes from the UK, Canada, and Europe, with few studies focusing on the United States. Given differences in healthcare structure and delivery, understanding context-specific barriers and facilitators warrants greater attention. Second, few studies have tested implementation strategies to increase uptake. To date, few studies have systematically examined how to promote the uptake of prehabilitation in routine cancer care. This protocol addresses that gap by pursuing three objectives: Objective 1: Identify cancer care guidelines and consensus documents that include prehabilitation and synthesize the prehabilitation recommendations. Objective 2: Map the reported barriers and facilitators to implementing cancer prehabilitation in the United States context. Objective 3: Identify and classify empirically tested implementation strategies aimed at enhancing prehabilitation uptake, and summarize evidence on their effectiveness, when applicable.
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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 | 0.065 | 0.107 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".