Defining high-quality palliative care in oncology practice: An ASCO/AAHPM Guidance Statement.
Bibliographic record
Abstract
108 Background: When integrated into routine oncology care, palliative care can improve symptom burden, quality of life, and patient and caregiver satisfaction. However, not all oncology practices have access to subspecialty palliative medicine for referral. This project endeavored to define what constitutes high-quality primary palliative care as delivered by medical oncology practices. Methods: An expert steering committee developed a list of 966 palliative care service items, divided into nine domains, each describing an aspect of palliative care delivery for patients with advanced cancer. Using a modified Delphi methodology, 31 multi-disciplinary panelists ranked each service item according to importance, feasibility and scope. Results: Panelists endorsed the highest proportion of palliative care service items in the domains of End-of-Life Care (81%); Communication and Shared Decision-Making (79%); and Care Planning (78%). Lowest proportions were in: Spiritual and Cultural Assessment and Management (35%) and Psychosocial Assessment and Management (39%). In the largest domain, Symptom Assessment and Management, there was consensus that all symptoms should be assessed and managed at a basic level with more comprehensive management for common symptoms such as nausea, vomiting, diarrhea, dyspnea and pain. Under the domain of Appropriate Palliative Care and Hospice Referral, there was consensus that oncology practices should be able to describe the difference between palliative care and hospice to patients and refer patients with an expected survival of under 3 months or poor performance status (Zubrod 3-4) to hospice. Conclusions: This statement describes high-quality palliative care for patients with advanced cancer or high symptom burden, as delivered by oncology practices. Oncology providers wishing to enhance palliative care delivery may find the guidance useful to inform operational changes and quality improvement efforts.
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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 | 0.055 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".