Disparities in access to palliative care services among cancer patients in south Texas.
Bibliographic record
Abstract
130 Background: Palliative medicine is dedicated to improving quality of life for patients throughout their disease course. The integration of palliative medicine in oncology practice has increased due to growing evidence of benefits for patient. While the collaboration of palliative and oncology care is beneficial, access to palliative services remains an issue for cancer patients. The aim of this project is to evaluate how insurance status impacts palliative care access for cancer patients in South Texas. Methods: During a 5-month study period, 607 patients from medical oncology clinics were evaluated based on their Edmonton Symptom Assessment System (ESAS) scores collected during their clinic visit to determine symptom burden and palliative care need. If providers saw a need, palliative care referrals were made. 27 unique patients received a total of 32 referrals over the study period. We examine time to initial palliative care appointment after the first referral was made. We compare time to consult based on where patients were able to receive palliative services. Location of care was based on type of insurance coverage. Results: During our study period, 13 patients were eligible to receive care through the University of Texas (UT) Medicine System (Group A) and 14 patients qualified for care at University System, which provides services to under-insured and safety net patients (Group B). Of the 27 referred patients, 85% made efforts to seek palliative care (n = 23), of which 78% had a palliative care visit (n = 18). On average, patients seeking care at the safety net facility (Group B) had 29.8 days longer wait times until their first potential visit (p < 0.01) compared to patients eligible for UT Medicine (Group A). Time to actual visit was on average 39.2 days longer for patients in Group B compared to patients in Group A (p = 0.02), averaging at 55.7 days for patients seeking care at the safety net facility. Conclusions: This investigation revealed a 4-fold increase in time to scheduled visit depending on insurance coverage. While this study is limited by a small sample, data suggests that under-insured oncology patients may have significantly different access to palliative care services which may impact the quality of cancer care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".