Cost Analysis of a Digital Multimodal Cancer Prehabilitation
Bibliographic record
Abstract
INTRODUCTION: There is growing evidence that prehabilitation programmes effectively improve the physical and psychological conditions of cancer patients awaiting treatment. During the pandemic, people with cancer were classed as vulnerable. To reduce risk to this population Kent and Medway Prehabilitation service transformed into a TeleHealth format. The aim of this study is to assess the impact on health-related quality of life (HRQoL) and the costs of a digital multimodal prehabilitation programme. METHODS: HRQoL was measured with the EQ-5D and quality-adjusted life years (QALYs) were calculated. Costs of the prehabilitation service and inpatient care were calculated. Comparisons were made between different levels of prehabilitation received. RESULTS: A sample of 192 individuals was included in the study Mean HRQoL improved from 69.53 at baseline to 85.71 post-rehabilitation, a 23% increase. For each additional week of prehabilitation care in cancer patients, the model predicts that the total QALYS increase by 0.02, when baseline utility is held constant. CONCLUSIONS: Prehabilitation is associated with improved HRQoL and QALYs. Our model of a multimodal digital prehabilitation program can be beneficial for patients and reduce costs for healthcare facilities even when the patients attend only a few sessions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".