Successes and Challenges of Implementing Teleprehabilitation for Onco-Surgical Candidates and Patients’ Experience: A Retrospective Pilot-Cohort Study
Bibliographic record
Abstract
Abstract Purpose: This study aimed to document the successes and challenges of teleprehabilitation programs for cancer patients undergoing surgery.Method: This pilot-cohort study included adults scheduled for elective cancer surgery, referred to the prehabilitation clinic to engage in physical activity and received a teleprehabilitation program between August 1st 2020 and February 28th 2021. Using a technology platform that included a tablet and was wearable, data were acquired through virtual physical activity monitoring in addition to patient charts.Results: Ten patients (8 males and 2 females; mean age: 68.3 years, SD: 11.96) diagnosed with various thoraco-abdominal malignancies were included in the current descriptive study. The successes identified were related to recruitment and assessment, improvement in functional capacity, clinic scheduling and interventions, and optimal medical follow-up. The challenges identified were related to the adoption of the technologies by patients and the multidisciplinary team, the accurate acquisition of patient physical activity data, and the initial costs to acquire the new technologies. Patients were satisfied with the teleprehabilitation program (i.e., services delivered; average appreciation: 96%), and they perceived the technologies provided to be 90% user-friendly.Conclusion: The findings of the current study are paramount in view of the current international health paradigm changes prioritizing remote interventions facilitated through digital communication technologies. It provides important insight into the clinical application of telehealth in elderly populations, notably in the context of acute preoperative cancer care. This article may provide guidance for other cancer care facilities aiming to implement teleprehabilitation programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".