Development of an eHealth Tool for Capturing and Analyzing the Immune-related Adverse Events (irAEs) in Cancer Treatment
Bibliographic record
Abstract
Introduction: Immunotherapy has revolutionized the treatment of many different types of cancer, but it is associated with a myriad of immune-related adverse events (irAEs). Patient-reported outcome (PRO) measures have been identified as valuable tools for continuously collecting patient-centered data and are frequently used in oncology trials. However, few studies still research an ePRO follow-up approach on patients treated with Immunotherapy, potentially reflecting a lack of support services for this population. Methods: The team co-developed a digital platform (V-Care) using ePROs to create a new follow-up pathway for cancer patients receiving immunotherapy. To operationalize the first 3 phases of the CeHRes roadmap, we employed multiple methods that were integrated throughout the development process, rather than being performed in a linear fashion. The teams employed an agile approach in a dynamic and iterative manner, engaging key stakeholders throughout the process. Results: The development of the application was categorized into 2 phases: "user interface" (UI) and "user experience" (UX) designs. In the first phase, the pages of the application were segmented into general categories, and feedback from all stakeholders was received and used to modify the application. In phase 2, mock-up pages were developed and sent to the Figma website. Moreover, the Android Package Kit (APK) of the application was installed and tested multiple times on a mobile phone to proactively detect and fix any errors. After resolving some technical issues and adjusting errors on the Android version to improve the user experience, the iOS version of the application was developed. Discussion: By incorporating the latest technological developments, V-Care has enabled cancer patients to have access to more comprehensive and personalized care, allowing them to better manage their condition and be better informed about their health decisions. These advances have also enabled healthcare professionals to be better equipped with the knowledge and tools to provide more effective and efficient care. In addition, the advances in V-Care technology have allowed patients to connect with their healthcare providers more easily, providing a platform to facilitate communication and collaboration. Although usability testing is necessary to evaluate the efficacy and user experience of the app, it can be a significant investment of time and resources. Conclusion: The V-Care platform can be used to investigate the reported symptoms experienced by cancer patients receiving Immune checkpoint inhibitors (ICIs) and to compare them with the results from clinical trials. Furthermore, the project will utilize ePRO tools to collect symptoms from patients and provide insight into whether the reported symptoms are linked to the treatment. Clinical Relevance: V-Care provides a secure, easy-to-use interface for patient-clinician communication and data exchange. Its clinical system stores and manages patient data in a secure environment, while its clinical decision support system helps clinicians make decisions that are more informed, efficient, and cost-effective. This system has the potential to improve patient safety and quality of care, while also helping to reduce healthcare costs.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".