Feasibility of Implementation and the Impact of a Digital Prehabilitation Service in Patients Undergoing Treatment for Oesophago-Gastric Cancer
Bibliographic record
Abstract
Background: Home-based and supervised prehabilitation programmes are shown to have a positive impact on outcomes in patients with oesophago-gastric (OG) cancer. The primary aim of this study was to establish the feasibility of delivering a digital prehabilitation service. Methods: Patients undergoing treatment for OG cancer with curative intent were recruited into the study. During the COVID-19 pandemic, patients were offered a digital prehabilitation service. Following the lifting of COVID-19 restrictions, patients were also offered both a hybrid clinic-based in-person service and a digital service. Implementation and clinical metrics from the two prehabilitation models were compared. Results: 31 of 41 patients accepted the digital service (75%). Of the people who started the digital programme, 3 dropped out (10%). Compliance with the weekly touchpoints was 86%, and the median length of programme was 12 weeks. Twenty-six patients enrolled in the in-person service. Two patients dropped out (10%). Average compliance to weekly touchpoints was 71%, and the median length of programme was 10 weeks. In the digital group, sit to stand (STS) increased from 14.5 (IQR 10.5–15.5) to 16 (IQR 16–22); p = 0.02. Median heart rate recovery (HRR) increased from 10.5 (IQR 7.5–14) to 15.5 (IQR 11–20) bpm; p = 0.24. There was a significant drop in distress (median 3 (IQR 0–5) to 1 (IQR 0–2); p = 0.04) and a small drop in anxiety (median 3 (0–5) to 2 (0–3); p = 0.22). There was no difference in the postoperative complication rate and length of hospital stay between the two groups. Discussion: This study has shown that digital prehabilitation can be delivered effectively to patients with OG cancer, with high engagement and retention rates. We observed improvements in some physical and psychological parameters with the digital service, with comparable clinical outcomes to the in-person service.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".