Symptom Burden and Palliative Referral Disparities in an Ambulatory South Texas Cancer Center
Bibliographic record
Abstract
Background: The American Society of Clinical Oncology’s recommendation for “dedicated palliative care services, early in the disease course, concurrent with active treatment” for cancer patients is a challenge for cancer centers to accommodate. Despite demonstrated benefits of concurrent care, disparities among socioeconomic and ethnic groups in access to supportive care services have been described. The aim of this project was to evaluate: a) how insurance coverage and ethnicity impact patient symptom burden and, b) how those factors influence palliative access for patients at a South Texas NCI-designated cancer center. Methods: During a 5-month retrospective period, 604 patients from five ambulatory oncology clinics completed the 10 question Edmonton Symptom Assessment Scale (ESAS) surveys during their clinic visit. Patient demographics, ESAS scores, palliative referral decisions, and time to palliative encounters were collected. We compared symptom burden and time to consult based on ethnicity and insurance status (insured = Group A; under-insured and safety net = Group B). Results: The mean ESAS score for all patients at the initial visit was 19.9 (SD=18.1). Safety net patients were significantly more likely to be Hispanic, younger in age, and have an underlying GI malignancy in comparison to insured patients; however, the symptom severity was similar between groups with over 40% of individuals reporting at least one severe symptom. Twenty-one referrals were made to palliative care. On average, Group B had 33.3 days longer wait times until their first potential visit (p<0.01) when compared to Group A. Time to actual visit was on average 57.6 days longer for patients in Group B compared to patients in Group A (p=0.01), averaging at 73.8 days for safety net patients. Conclusions: This project highlights the high symptom burden of oncology patients and disparities in access to services based on insurance coverage. This investigation revealed a 4-fold increase in the time to the first scheduled palliative care visit based on whether patients were insured versus under-insured. While this study is limited by a small sample size, data suggest that under-insured oncology patients may have significant barriers to palliative care services, which may influence their cancer care quality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".