Outpatient Embedded Palliative Care for Patients with Advanced Thoracic Malignancy: A Retrospective Cohort Study
Bibliographic record
Abstract
Although cancer care is often contextualized in terms of survival, there are other important cancer care outcomes, such as quality of life and cost of care. The ASCO Value Framework assesses the value of cancer therapies not only in terms of survival but also with consideration of quality of life and financial cost. Early palliative care for patients with advanced cancer is associated with improved quality of life, mood, symptoms, and overall survival for patients, as well as cost savings. While palliative care has been shown to have numerous benefits, the impact of real-world implementation of outpatient embedded palliative care on value-based metrics is not fully understood. We sought to describe the association between outpatient embedded palliative care in a multidisciplinary thoracic oncology clinic and inpatient value-based metrics. We performed a retrospective cohort study of 215 patients being treated for advanced thoracic malignancies with non-curative intent. We evaluated the association between outpatient embedded palliative care and inpatient clinical outcomes including emergency room visits, hospitalizations, intensive care unit admissions, hospital charges, as well as hospital quality metrics including 30-day readmissions, admissions within 30 days of death, inpatient mortality, and inpatient hospital charges. Outpatient embedded palliative care was associated with lower hospital charges per day (USD 3807 vs. USD 4695, p = 0.024). Furthermore, patients who received outpatient embedded palliative care had lower hospital admissions within 30 days of death (O.R. 0.45; 95% CI 0.29, 0.68; p < 0.001) and a lower inpatient mortality rate (IRR 0.67; 95% CI 0.48, 0.95; p = 0.024). Our study further supports that outpatient palliative care is a high-value intervention and alternative models of palliative care, including one embedded into a multidisciplinary thoracic oncology clinic, is associated with improved value-based metrics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".