Standards, Guidelines, and Quality Measures for Successful Specialty Palliative Care Integration Into Oncology: Current Approaches and Future Directions
Bibliographic record
Abstract
Although robust evidence demonstrates that specialty palliative care integrated into oncology care improves patient and health system outcomes, few clinicians are familiar with the standards, guidelines, and quality measures related to integration. These types of guidance outline principles of best practice and provide a framework for assessing the fidelity of their implementation. Significant advances in the understanding of effective methods and procedures to guide integration of specialty palliative care into oncology have led to a proliferation of guidance documents around the world, with several areas of commonality but also some key differences. Commonalities originate from a shared vision for integration; differences arise from diverse roles of palliative care specialists within cancer care globally. In this review we discuss three of the most cited standards/guidelines, as well as quality measures related to integrated palliative and oncology care. We also recommend changes to the quality measurement framework for palliative care and a new way to match palliative care services to patients with advanced cancer on the basis of care complexity and patient needs, irrespective of prognosis.
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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 | 0.086 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".