Improving End-of-Life Care: Palliative Care Embedded in an Oncology Clinic Specializing in Targeted and Immune-Based Therapies
Bibliographic record
Abstract
PURPOSE: Patients with advanced cancer benefit from early involvement of palliative care. The ideal method of palliative care integration remains to be determined, as does its effectiveness for patients treated with targeted and immune-based therapies. MATERIALS AND METHODS: We studied the impact of an embedded palliative care team that saw patients in an academic oncology clinic specializing in targeted and immune-based therapies. Patients seen on a specific day accessed the embedded model, on the basis of automatic criteria; patients seen other days could be referred to a separate palliative care clinic (usual care). We abstracted data from the medical records of 114 patients who died during the 3 years after this model's implementation. RESULTS: Compared with usual care (n = 88), patients with access to the embedded model (n = 26) encountered palliative care as outpatients more often ( P = .003) and earlier (mean, 231 v 109 days before death; P < .001). Hospice enrollment rates were similar ( P = .303), but duration was doubled (mean, 57 v 25 days; P = .006), and enrollment > 7 days before death-a core Quality Oncology Practice Initiative metric-was higher in the embedded model (odds ratio, 5.60; P = .034). Place of death ( P = .505) and end-of-life chemotherapy (odds ratio, 0.361; P = .204) did not differ between the two arms. CONCLUSION: A model of embedded and automatically triggered palliative care among patients treated exclusively with targeted and immune-based therapies was associated with significant improvements in use and timing of palliative care and hospice, compared with usual practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".