Trends toward declining aggressive care at end of life among older cancer patients in the Deep South.
Bibliographic record
Abstract
e20558 Background: Healthcare utilization and costs escalate dramatically in the final months of life. However, it is not clear on a population level how these expenses distribute between therapies that are focused on life extension and those that are more palliative in orientation. Methods: Claims data were obtained forMedicare beneficiaries ≥ 65 years old diagnosed with cancer within the UAB Health System Cancer Community Network (UAB CCN) from 2008-2014. For 10 quarters between January 2012 and June 2014, we determined the proportion of deceased beneficiaries who had aggressive care including ER visits, hospitalizations, ICU admissions, or chemotherapy in the last 2 weeks of life, and hospice utilization (admission or less than 3 days of hospice) in the quarter when death occurred. A lay navigation program with an emphasis on palliative care was implemented in the UAB CCN in the second quarter of 2013. Patients in the Program and those who were not were included in this analysis. Descriptive analyses and linear regression were used to test for trends over time. Results: Among 5,861 decedents included in the analyses, over the 10 quarters events in the last 2 weeks of life ranged from 18.5% to 15.8% for ICU admissions (14.6% decrease, p = 0.11), from 39.2% to 32.0% for ER visits (18.4% decrease, p = 0.03), and from 4.7% to 3.5% for chemotherapy (25.5% decrease, p = 0.11). Over the 10 quarters, hospice enrollment ranged from 70.7% to 77.4% (9.48% increase; p = 0.06), and the proportion of patients on hospice for less than 3 days changed from 7.8% to 7.5% (3.85% decrease, p = 0.30). Conclusions: Despitenational trends demonstrating increasingly aggressive care at end of life, in the UAB CCN there was a consistent trend toward decreased aggressive care and increased hospice care. Future efforts are needed to increase access to palliative care to reduce aggressive care and increase hospice care. The project described was supported by Grant Number 1C1CMS331023 from the Department of Health and Human Services, Centers for Medicare & Medicaid Services. The contents of this abstract are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services or any of its agencies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".