Efficacy of a Digital Postoperative Rehabilitation Intervention in Patients With Primary Liver Cancer: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Rehabilitation is considered a fundamental component of cancer treatment, especially for patients undergoing cancer surgery. In contrast to conventional rehabilitation education, digital rehabilitation has the potential to improve patients' access to postoperative rehabilitation programs. While digital health has rapidly emerged to aid patients with various diseases, their clinical efficacy in the recovery of patients with primary liver cancer (PLC) undergoing hepatectomy remains inadequately investigated. Objective: This study aims to evaluate whether a digital postoperative rehabilitation intervention is efficient in improving physical fitness, enhancing exercise adherence, and alleviating fatigue among patients with PLC after hepatectomy. Methods: A randomized controlled trial was undertaken across 2 university-affiliated hospitals in Eastern China. A total of 100 participants were enrolled in this study and were allocated randomly to either the digital health (intervention group, n=50) or the rehabilitation manual-based group (control group, n=50) at a 1:1 ratio. Patients were unblinded and prospectively followed for the intervention of 3 weeks. Outcome measures included physical fitness, exercise adherence, and status of fatigue. Results: Overall, 91 out of 100 patients completed the research and were evaluated after 3 weeks of intervention. The digital health group showed better cardiopulmonary endurance than the control group. The mean difference in the change of 6-minute walk test distance from baseline between the groups was 70.21 (95% CI 0.730-82.869) m (P=.05). No statistically significant effects were found for grip strength, 5-repetition-sit-to-stand test time, and fatigue. The exercise adherence in the digital health group was higher than that in the control group (χ22=15.871, P<.001). Conclusions: The findings suggested that the implementation of digital health had a positive impact on recovery in exercise capacity after hepatectomy. In addition, rehabilitation exercise mode based on digital health has the potential to improve the exercise adherence of patients with PLC compared to conventional manual-based rehabilitation guidance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".