Prehabilitation for Patients with Cancer Undergoing Radiation Therapy: a Scoping Review
Bibliographic record
Abstract
AIMS: Prehabilitation is a process of identifying and assessing factors that could compromise the physical and psychological health of patients undergoing cancer treatment and implementing an intervention to combat such concerns. The use of prehabilitation in cancer surgery has yielded positive outcomes in rectal, lung and abdominal cancers. Prehabilitation strategies have potential to improve the management of patients receiving radiation therapy or chemoradiation. The aim of the present study was to map the evidence of the assessment and evaluation of prehabilitation for radiation therapy patients. MATERIALS AND METHODS: A database search using EMBASE and PubMed was conducted. The PRISMA guidelines were adhered to. Keywords included prehabilitation, radiation therapy/radiotherapy, chemoradiotherapy/chemoradiation, intervention and exercise. Types of prehabilitation strategy, their purposes and impact, according to cancer site, were analysed. RESULTS: Prehabilitation is most commonly evaluated in head and neck cancer, whereby unimodal, physical interventions manage dysphagia. Prehabilitation for lung cancer demonstrated its ability to widen treatment options for patients. Physical prehabilitation is administered to combat adverse effects of neoadjuvant chemoradiation therapy in patients with rectal cancer. CONCLUSION: Prehabilitation is adaptive and tailored to specific patient and site needs; thus it is applied across a wide range of cancer sites. More interventions by which radiation therapy is the definitive treatment modality and larger sample sizes within these studies are warranted to increase prehabilitation utilisation for patients undergoing radiation therapy.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".