App-Controlled Treatment Monitoring and Support for Head and Neck Cancer Patients (APCOT): Protocol for a Prospective Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Head and neck cancers (HNCs) are among the most common malignancies, which often require multimodal treatment that includes radiation therapy and chemotherapy. Patients with HNC have a high burden of symptoms due to both the damaging effects of the tumor and the aggressive multimodal treatment. Close symptom monitoring over the course of the disease may help to identify patients in need of medical interventions. OBJECTIVE: This APCOT (App-Controlled Treatment Monitoring and Support for Head and Neck Cancer Patients) trial is designed to assess the feasibility of monitoring HNC patients during the course of (chemo)radiation therapy daily using a mobile app. Additionally, symptom patterns, patient satisfaction, and quality of life will be measured in app-monitored patients in comparison to a patient cohort receiving standard-of-care physician appointments, and health economy aspects of app monitoring will be analyzed. METHODS: This prospective randomized single-center trial will evaluate the feasibility of integrating electronic patient-reported outcome measures (ePROMs) into the treatment workflow of HNC patients. Patients undergoing definitive or adjuvant (chemo)radiation therapy as part of their HNC treatment at the Department of Radiation Oncology, University Medical Center Freiburg (Freiburg, Germany) will receive weekly physician appointments and additional appointments as requested to monitor and potentially treat symptoms during the course of treatment. Patients in the experimental arm will additionally be monitored daily using a dedicated app regarding their disease- and treatment-related symptoms, quality of life, and need for personal physician appointments. The feasibility of ePROM monitoring will be tested as the primary endpoint and will be defined if ≥80% of enrolled patients have answered ≥80% of their daily app-based questions. Quality of life will be assessed using the validated European Organisation for Research and Treatment of Cancer questionnaires, and patient satisfaction will be measured by the validated Patient Satisfaction Questionnaire Short Form at the initiation, in the middle, and at completion of radiation therapy, as well as at follow-up examinations. Additionally, the number and duration of physician appointments during the course of radiation therapy will be quantified for both ePROM-monitored and standard-of-care patients. RESULTS: This trial will enroll 100 patients who will be randomized (1:1) between the experimental arm with ePROM monitoring and the control arm with standard patient care. Recruitment will take 18 months, and trial completion is planned at 24 months after enrollment of the last patient. CONCLUSIONS: This trial will establish the feasibility of close ePROM monitoring of HNC patients undergoing (chemo)radiation therapy. The results can form the basis for further trials investigating potential clinical benefits of detailed symptom monitoring and patient-centered care in HNC patients regarding oncologic outcomes and quality of life. TRIAL REGISTRATION: German Clinical Trials Register DRKS00020491; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00020491. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/21693.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".