Oncologic Therapy Support Via Means of a Dedicated Mobile App (OPTIMISE-1): Protocol for a Prospective Pilot Trial
Bibliographic record
Abstract
BACKGROUND: The increasing role of consumer electronics and Web-enabled mobile devices in the medical sector opens up promising possibilities for integrating novel technical solutions into therapy and patient support for oncologic illnesses. A recent survey carried out at Heidelberg University Hospital suggested a high acceptance among patients for an additional approach to patient care during radiotherapy based on patient-reported outcomes by a dedicated mobile app. OBJECTIVE: The aim of this trial (OPTIMISE-1: Oncologic Therapy Support Via Means of a Dedicated Mobile App - A Prospective Feasibility Evaluation) is to prospectively evaluate the feasibility of employing a mobile app for the systematic support of radiooncological patients throughout the course of their radiotherapy by monitoring symptoms and patient performance, and facilitating the background-exchange of relevant information between patient and physician. METHODS: The present single-center, prospective, exploratory trial, conducted at Heidelberg University Hospital, assesses the feasibility of integrating an app-based approach into patient-care during radiotherapy. Patients undergoing curative radiotherapy for thoracic or pelvic tumors will be surveyed regarding general performance, treatment-related quality of life (QoL) and symptoms, and their need to personally consult a physician by means of a mobile app during treatment. The primary endpoint of feasibility will be reached when 80% of the patients have successfully answered 80% of their respective questions scheduled for each treatment day. Furthermore, treatment-related patient satisfaction and health-related QoL is assessed by the Patient Satisfaction Questionnaire Short Form (PSQ-18) and the European Organization for Research and Treatment of Cancer (EORTC) questionnaires at the beginning (baseline) and end of radiotherapy, and at the first follow-up. RESULTS: This trial will recruit 50 patients over a period of 12 months. Follow-up will be completed after 18 months, and publication of results is planned at 24 months after trial initiation. CONCLUSIONS: This study will serve as a basis for future studies aiming to exploit the constant innovation in mobile medical appliances and integrate novel patient-centered concepts into patient care in the context of radiotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT03168048; https://clinicaltrials.gov/ct2/show/NCT03168048 (Archived at WebCite http://www.webcitation.org/6wtWGgi0X).
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.015 |
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".