Care at Your Fingertips: Codesign, Development, and Evaluation of the Oncology Hub App for Remote Symptom Management in Pediatric Oncology
Bibliographic record
Abstract
PURPOSE: To codesign, develop, and evaluate a smartphone app that includes patient-reported measures of symptoms and real-time advice in children's cancer. METHODS: The Oncology Hub is a comprehensive approach to symptom management that includes a suite of codesigned tools and resources including clinical algorithms to determine the level of concern, symptom management advice, and resources for families of children with cancer. The evaluation involved Think Aloud interviews with parent and adolescent patients to complete tasks in the app as well as a User Experience questionnaire (score range, 0-120) and qualitative feedback. The accuracy of algorithms was determined by repeated testing of inputs and outputs over 4 weeks. RESULTS: Design and wireframes were iteratively refined through consultation with parents and adolescents confirming the final design. Beta testing evaluation was then completed by 25 participants including two adolescents. Across all participants, 84% of tasks were easy to navigate, and the Oncology Hub demonstrated high usability, usefulness, and acceptability with participants' scores ranging between 90 and 120 (mean = 112.2, standard deviation = 9.43). Qualitative feedback was positive. Testing of algorithms identified inconsistencies in understanding between clinical research and coding teams; refinements were made until the expected response notifications were returned with 100% accuracy. CONCLUSION: Technology offers new ways to think about how clinicians and families communicate and share information to harness the best of community and hospital services. Understanding how information is exchanged using health apps, and how this affects clinical workflow is critical to successful implementation, and optimizing symptom assessment and management in children with cancer.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".