Development of A Patient-Centered Symptom Management Mobile Application
Bibliographic record
Abstract
The evolution of Patient Reported Outcomes (PROs), has made an essential impact on patient-centered symptom management. PROs enable us to measure the patient‘s feels about their symptoms during treatment. ePROs (electronic PROs) are interfaces that allow a patient or health care provider to manage symptoms using an application such as mobile computing applications. The growth of mobile technologies in the healthcare sector has enabled us to take advantage of features like data manipulation, portability and standardization enable a better patient-driven symptom management. The Pan-Canadian Oncology Symptom Triage and Remote Support (COSTaRS) is a paper-based symptom management guideline designated for nurses. The objective of COSTaRS is to help and improve the decision-making process and create a consistent symptom management reporting system. Although this tool introduces numerous advantages in cancer symptom management, it also induces a number of issues for patients due to being overwhelming. Moreover, a noticeable portion of drawbacks originates from the paper-based nature of COSTaRS. In addition, cancer care symptom management mobile applications do not offer proper evidence-based centered symptom management system to the users. The purpose of this study is to design and developed the mobile version of COSTaRS for patients and caregivers. We identify problems with the current paper-based structure and related academic and non-academic works and then, we design and evaluate a mobile version of COSTaRS that takes advantage of advances in mobile technology. We leverage COSTaRS knowledge to create a mobile application for symptom management. We create an evidence-based platform for cancer treatment-related symptom management. A usability testing has been conducted for evaluation of the COSTaRS mobile application. The results of this study verify the usability of COSTaRS mobile application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".