A prototype mobile application to improve communication about symptom management.
Bibliographic record
Abstract
27 Background: Cancer patients report many physical and emotional symptoms which can go unreported and underestimated resulting in unmet needs. Research suggests systematic collection of symptom data is associated with decreased emergency department use, increased quality of life, treatment toleration and overall survival. The multiple myeloma (MM) patient population is noted to have high symptom burden and represent an important target for intervention. This project aimed to develop a prototype app to facilitate MM patient/clinician communication about symptom management. Methods: 15 MM patients and 11 MM clinicians were interviewed to better understand patients’ symptom experience and management practices and preferences. Insights gained guided development of a prototype MM Coach mobile app. The think aloud protocol and cognitive interviewing were used to test usability and the prototype was iteratively refined. Results: Subjects highlighted a need for better symptom tracking over time, medication adherence tools, and real-time feedback to help patients self-manage symptoms. Our prototype app contains several modules designed to facilitate MM patient symptom management. 1) Track Symptoms; Using the Edmonton Symptom Assessment Scale patients track bothersome symptoms whenever they occur. 2) Track Medications; Patients can set up medication alerts and log medication use. 3) Track Mood; Patients record and track their distress level using the Distress Thermometer. 4) Relaxation Tools; This module contains a number of useful mind body activities such as guided imagery. 5) Get Support; Links to MM and non-MM related sources of support. 6) Prepare for Appointments; This module facilitates patients’ prioritizing issues to facilitate productive clinical encounters. 7) Insights; Patients and clinicians can review trends in symptom burden and medication adherence. 8) Learn; Educational content on topics relevant to MM symptoms such as pain, fatigue, depression. Conclusions: Our team is currently working with mobile app developers to build a version for the iOS AppStore and Android GooglePlay store. A pilot will be conducted to evaluate acceptability and feasibility in preparation for a clinical trial.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".