Symptom trajectory for patients utilizing remote symptom monitoring during gastrointestinal cancer treatment.
Bibliographic record
Abstract
345 Background: Use of electronic patient-reported outcome data allows patients to report symptoms in real time. This analysis aims to better understand the trajectory for symptoms reported via Remote Symptom Monitoring (RSM) by patients receiving gastrointestinal (GI) cancer treatment. Methods: This retrospective cohort study included patients initiating GI cancer treatment (chemotherapy, targeted therapy, and immunotherapy) between August 2022 and April 2023 at USA Health Mitchell Cancer Institute (MCI). Patients were eligible if they were starting a new treatment or had started treatment in the past 30 days. Patients received a weekly symptom survey over the course of 24 weeks through text message or e-mail. Patients were monitored for a total of 24 weeks regardless of their enrollment date. Patients reported symptoms as mild, moderate, severe, or very severe. All moderate and severe alerts were sent to the clinical nurse via the electronic medical record (EMR) to be acknowledged. After enrollment, health information including age, race, sex, zip code, ethnicity, cancer type, and cancer stage were extracted from the EMR. Descriptive statistics were calculated to examine frequencies of reported symptoms and their severity over time. Results: Of 75 GI patients approached, 63 patients (84%) were enrolled in RSM; 44% were female; 30% of patients were Black or African American, and median age was 65 (range 30-82). GI cancer types varied; Pancreatic (21%), Rectal (13%), Colorectal (14%), Colon (13%), Gastric (11%), Liver (11%), and other (17%). Over 24 weeks, 424 alerts were reported. Pain (31%), nausea and vomiting (21%), and decreased appetite (17%) were the most reported symptoms. 311 alerts were moderate (73%) and 113 were severe (27%). At week 0 (baseline; n = 63) 37 moderate alerts and 21 severe alerts were reported. At week 24 (final week; n = 63) 7 moderate alerts and 6 severe alerts were reported. Overall, there was a decreasing trajectory from week to week for moderate and severe alerts, with outliers noted at weeks 8 and 20. Conclusions: Findings suggest that RSM allows for an improvement in symptom trajectory for GI cancer patients based on the decrease in moderate and severe alerts reported from baseline to week 24. This decrease suggests that reported symptoms are being appropriately monitored and addressed by the patient's clinical care team due to improvement of symptoms or improvement of symptom management. Future research is needed to determine the benefits of prolonged RSM utilization by patient-reported quality of life as well as survival rate.
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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 | 0.000 | 0.006 |
| 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".