Trajectory of symptoms reported in remote symptom monitoring over the course of oncology treatment for lung cancer.
Bibliographic record
Abstract
250 Background: Electronic patient reported outcomes (ePROs) enable patients to report symptoms from treatment in real time using their mobile device. This analysis sought to better understand the trajectory of reported symptoms via remote symptom monitoring (RSM) during treatment for patients with lung cancer. Methods: We approached patients with lung cancer initiating treatment at the Mitchell Cancer Institute (MCI) between March 2022-October 2023 to participate in an RSM program. Patients were eligible if they were initiating treatment (chemotherapy, targeted therapy, or immunotherapy) for the first time at MCI. Patients seeking a second opinion were excluded. Enrolled patients received a symptom survey (PRO-CTCAE questions) once a week via text or email. Alerts were forwarded to the clinical care team for symptom management. Patients completed symptom assessments for 24 weeks or until withdrawal. At 24 weeks, patients were given the ability to continue symptom assessments if they were continuing treatment. Patient demographics including age at enrollment, race, sex, cancer type, cancer stage, and PRO data were collected from electronic health records and the PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and median and interquartile ranges (IQR) for continuous variables. Results: Of 80 patients approached, a total of 57 (71%) patients with lung cancer were enrolled in RSM; 20% were Black or African American and 80% were White; median age was 66 (IQR 61-73). Over 24 weeks, 732 symptom alerts were reported; 75% considered moderate and 25% considered severe. Overall, the most frequently reported symptom was pain (29%), followed by dyspnea/cough (25%) and constipation (13%). At baseline (week 0), 78 moderate symptoms and 39 severe symptoms alerts were reported. At week 24, 10 moderate symptoms and 4 severe symptoms alerts were reported. Overall, there was a decrease in symptom alerts over time for both moderate and severe alerts. Specific symptom trajectories followed similar patterns. Conclusions: In our sample, most symptoms were reported during the initial three months of treatment with a subsequent decrease in symptom alerts over time, indicating effective monitoring and management by clinical teams engaged in RSM. Future research is needed to understand if symptom improvement correlates with enhanced quality of life, reduced hospitalizations, and prolonged survival, as well as a lessened burden of call volume on the clinical team. Future analyses should also be done to compare similar results of different disease types.
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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 | 0.001 | 0.001 |
| 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".