<scp>Patient‐reported</scp> symptom burden in routine oncology care: Examining racial and ethnic disparities
Bibliographic record
Abstract
BACKGROUND: Racial and ethnic disparities are well-documented in cancer outcomes such as disease progression and survival, but less is known regarding potential disparities in symptom burden. AIMS: The goal of this retrospective study was to examine differences in symptom burden by race and ethnicity in a large sample of cancer patients. We hypothesized that racial and ethnic minority patients would report greater symptom burden than non-Hispanic and White patients. METHODS AND RESULTS: A total of 5798 cancer patients completed the Edmonton Symptom Assessment Scale-revised (ESAS-r-CSS) at least once as part of clinical care. Two indicators of symptom burden were evaluated: (1) total ESAS-r-CSS score (i.e., overall symptom burden) and (2) number of severe symptoms (i.e., severe symptomatology). For patients completing the ESAS-r-CSS on multiple occasions, the highest score for each indicator was used. Zero-inflated negative binomial regression analyses were conducted, adjusting for other sociodemographic and clinical characteristics. Symptomology varied across race. Patients who self-identified as Black reported higher symptom burden (p = .016) and were more likely to report severe symptoms (p < .001) than self-identified White patients. Patients with "other" race were also more likely to report severe symptoms than White patients (p = .032), but reported similar total symptom burden (p = .315). Asian and Hispanic patients did not differ from White or non-Hispanic patients on symptom burden (ps > .05). CONCLUSION: This study describes racial disparities in patient-reported symptom burden during routine oncology care, primarily observed in Black patients. Clinic-based electronic symptom monitoring may be useful to detect high symptom burden, particularly in patients who self-identify their race as Black or other. Future research is needed to reduce symptom burden in racially diverse cancer populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".