Referral patterns and characteristics of uninsured versus insured patients referred to the outpatient supportive care center (SCC) at a comprehensive cancer center.
Bibliographic record
Abstract
116 Background: Multiple barriers exist in providing quality palliative care to low-income patients with cancer. Such disparities may negatively influence effective management of symptoms including pain. Our objective was to compare referral patterns and characteristics (level of symptom distress) of uninsured vs insured patients. Methods: We reviewed randomly selected charts of 100 Indigent (IND) and 100 Medicaid (MC) patients and compared them with a random sample of 300 patients with insurance (INS) referred during the same time period (1/2010 to 12/2014) to our SCC. Data was collected for date of registration at the cancer center, diagnosis of Advanced Cancer (ACD), first visit to the SCC (PC1), symptom assessment (Edmonton Symptom Assessment Scale-ESAS) at PC1. We excluded self-pay patients. Results: Results for IND, MC and INS (n = 481) respectively are as follows: Mean (SD) Age in yrs. was 50 (12), 48 (11) and 63 (13); p < 0.001. Percentage of non-white was 44%, 51% and 19.5%; p < 0.001. Percentage of unmarried patients was 64%, 68% and 33%; p < 0.001. Mean (SD) ESAS score at PC1 for pain was 5.6 (3.2), 6.7 (2.5), 4.9 (3.2); p < 0.001. Percentage of patients on opioids upon referral was 86%, 62%, and 54%; p < 0.001. Mean (SD) for referral time in months from ACD to PC1 was 8.7 (SD 10.4), 12.3 (SD 18.1) and 12 (SD 19.9) p = 0.31; for no. of encounters with SC per month were 0.46 (0.45), 0.41 (0.46) and 0.3 (0.55); p = 0.01; for survival in months (PC1 to last contact) was 6.4 (5.8), 5.6 (6.4) & 6 (7.22) p = 0.77. Conclusions: Uninsured patients had significantly higher levels of pain, were more frequently on opioids, younger, non-white and not married. They also required a larger number of SCC encounters. Insurance status did not impact timing of SCC referral or SCC follow ups at our cancer center.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".