Virtual integration of patient education in radiotherapy (VIPER)
Bibliographic record
Abstract
Purpose: Pre-radiotherapy patient education led by Radiation Therapists (RTT) has been shown to improve patients' distress and overall experiences. In an effort to offer a remote delivery method while allowing for visual learning and face-to-face communication, this pilot project evaluated the feasibility and acceptability of using virtual videoconferencing for patient education. Methods: This prospective pilot study integrated virtual patient education into standard care. This workflow consisted of a one-on-one, 45-minute tele-education session with an RTT on the day prior to CT-simulation. For this study, patients were offered the option to complete the session using web-based videoconferencing if they had the capability for it. Feasibility was evaluated as the proportion of patients who agreed to and completed virtual education. To evaluate acceptability, patients and RTTs were then emailed post-intervention surveys evaluating their satisfaction with virtual patient education. Results: Over three months 106 of 139 patients (76%) approached consented to virtual education. The median (range) age was 65 (27-93), 69% were male and most had genitourinary (38%) or head-and-neck (29%) cancers. Ninety patients (85%) completed virtual education as planned, with incompletions due to scheduling (8) or patient technical issues (7), or treatment cancellation (1). Sixty-eight patients completed surveys, with the vast majority agreeing virtual education was clear (94%) and helped them prepare (100%), they were comfortable with the technology (96%) and they were satisfied overall (99%). Twelve RTTs responded, suggesting overall that virtual education was higher quality though less feasible than tele-education, and comparable to in-person education. Conclusion: Offering individual, RTT-led virtual education using videoconferencing to patients pre-radiotherapy was feasible and acceptable in this pilot study, and is therefore being recommended as an option for all our patients. Future work will directly compare the effectiveness of in-person versus virtual education, and incorporate individual patient needs and preferences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".