Sociodemographic difference in patients who enroll and decline remote symptom monitoring (RSM).
Bibliographic record
Abstract
268 Background: Remote symptom monitoring (RSM) using patient-reported outcomes has been shown to reduce symptom burden and hospitalizations in clinical trials. However, little is known about how willing patients are to participate in remote symptom monitoring in real-world settings, particularly for vulnerable patient populations. This study aims to compare characteristics of cancer patients enrolled vs. patients who declined enrollment into RSM. Methods: This prospective study used data that assessed the characteristics of patients who enrolled vs. patients who declined enrollment into RSM. Inclusion criteria included participants’ age ≥18 with cancer who received chemotherapy, targeted therapy, or immunotherapy at the University of Alabama at Birmingham. Race and ethnicity (Black or African American, White, Asian, other and unknown), sex, cancer type (breast, gastrointestinal [GI], genitourinary [GU], gynecological [GYNX], head and neck, leukemia, lymphoma, melanoma, myeloma and other), urban/rural residence, Area Deprivation Index (ADI), and insurance type (Medicaid, Medicare, none, other and private) were abstracted from electronic medical records (EMR) and PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and medians and interquartile ranges for continuous variables. Differences in enrollment status characteristics were calculated using measures of effect size such as Cramer’s V. Results: Of the 307 patients, two thirds of patients were female (71%); 25% were Black or African American and 66% were White patients; 15% lived in an area of higher disadvantage. For insurance, 46%, 26%, 10%, 8%, and 9% of patients had Private, Medicare, Medicaid, other insurance, and no insurance, respectively. The proportion of patients who declined enrollment was higher for males than females (22% vs. 10%), Black or African American than White (18% vs 13%); and having Medicare than private insurance (22% vs. 10%). Compared to those who enrolled, patients who declined enrollment were more often to be male (V:0.2), Black or African American (V:0.1); and have Medicare insurance (V:0.2). Patients enrolled vs. declined in RSM had similar ADI scores (V:0.01). Conclusions: This study demonstrates that potentially vulnerable patients, including Black patients and those with public insurance, have lower RSM engagement. Future analysis is needed to understand participation barriers and how to better engage diverse populations to ensure optimal healthcare delivery to all patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".