Integrating Palliative Care Into the ICU: From Core Competency to Consultative Expertise
Bibliographic record
Abstract
OBJECTIVES: To propose a model describing levels of integration of palliative care into the care of ICU patients. DATA SOURCES: Literature review and author opinion. CONCLUSIONS: All critical care team members should demonstrate and foster their core competencies in caring for patients with complex illness and uncertain prognosis, including at the end of life. We describe these core competencies of the ICU team member as "primary" palliative care skills. Some ICU team members will have special expertise in end-of-life care or symptom management and decision-making support and will serve as local experts within the ICU team as a resource to other team members. We call this skillset "secondary" palliative care. Some patients will benefit from the full range of expertise provided by a separate consulting team, with additional training, focused on caring for patients with palliative care needs across the full spectrum of patient locations within a health system. We term the skillset provided by such outside consultants "tertiary" palliative care. Solutions for meeting patients' palliative care needs will be unique within each system and individual institution, depending on available resources, history, and structures in place. Providers from multiple professions will usually contribute to meeting patient needs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".