Safety and Efficacy of Telemedicine for Patients With Advanced Cancer in the Outpatient Setting: Lessons Learned From a Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Telemedicine (TM) was studied, particularly during the COVID-19 pandemic, to ascertain its utility in delivering remote medical services. STUDY QUESTION: What palliative care (PC) interventions can be provided through TM consultations compared with face-to-face (FF) consultations? What is their efficacy in reducing the intensity of suffering in the physical, emotional, social, and spiritual domains? What is the level of satisfaction with the care given? STUDY DESIGN: Randomized controlled trial with 2 arms: TM consultations using Zoom and WhatsApp secure platforms (Intervention group) and FF consultations (Control group). Participants received 8 scheduled weekly consultations and on-demand consultations. MEASURES AND OUTCOMES: The patients completed weekly Edmonton Symptom Assessment System, Problems and Needs in Palliative Care Short Form, and Patient Satisfaction Questionnaire Short Form monthly questionnaires. Statistical analyses were performed using GraphPad Prism 10.0.2. RESULTS: Between July 2023 and January 2024, 26 patients with newly diagnosed advanced cancer were randomized, 23 completed the study and 3 died in the TM arm (attrition rate 11.53%). Enrolled participants had predominantly advanced head and neck cancer (30.76%) and digestive tract cancer (23.07%). Patients in the TM arm had a lower performance status compared with the FF group. One thousand one hundred sixty-eight PC interventions were performed, 628 (FF) versus 540 (TM). In the physical domain, 343 versus 266; in the emotional domain, 219 versus 206; in the social domain, 18 versus 18; in the spiritual domain, 48 versus 50. Higher reductions in symptom intensity scores were reported in the TM arm (100% for depression, anxiety, hemorrhage, dysphagia, and secretions; >90% for pain, nausea, and appetite; >80% for sleep, dyspnea, and constipation; and >70% for cough), with statistical significance for pain ( P = 0.0140), nausea ( P = 0.0148), depression ( P = 0.0318), and constipation ( P = 0.0100). High satisfaction scores (>80, range 18-90) were reported for both arms. CONCLUSIONS: This exploratory pilot study shows that TM PC interventions are feasible and lead to high reductions in intensity scores for symptoms, with high satisfaction scores.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".