372 Defining and designing a remote monitoring tool for CAR T-cell therapy patients
Bibliographic record
Abstract
Objectives/Goals: The research aims to prototype a mobile app for physicians to remotely monitor patients receiving CD19-directed CAR T-cell therapy post-discharge. This app will facilitate standardized data collection across carious CAR-T treatment centers and help harmonize follow-up protocols. Methods/Study Population: A literature review and semi-structured interviews with patients, clinical coordinators, and experts helped identify essential parameters for a mobile app prototype aimed at monitoring adverse effects such as cytokine release syndrome and neurotoxicity. The app was designed through process mapping to combine data from self-reports and wearable devices, such as the Garmin smartwatch. New screens were designed in Figma, drawing from an existing patient monitoring app for allogeneic stem cell transplant follow-up. Finally, a preliminary feasibility study will be conducted to gather feedback on the app prototype from CAR T-cell therapy patients, healthcare providers, and stakeholders, ensuring its effectiveness and usability. Results/Anticipated Results: Semi-structured interviews with people with professional and lived experience with CAR T-cell therapy were conducted to determine what metrics might be monitored and how they could be measured remotely to effectively monitor for side effects. The mobile phone application was then prototyped using process mapping in Visio®, designed in Figma, and preliminary development was completed. The final prototype includes parameters that will be recorded or measured using a combination of self-reporting and devices to monitor body temperature, basic vitals, activity, sleep, and cognitive function, among others. The prototype of the remote monitoring app is the first step in implementing remote monitoring of CAR T patients, standardized data collection, and reduction in the overall cost of CAR T-cell therapy. Discussion/Significance of Impact: This app will enable physicians to monitor patients for routine follow-ups and adverse effects, such as CRS and ICANS. Future research will validate the digitized ICANS assessment and used to establish best practices for standardizing CAR-T follow-up protocols across Canada.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".