Patients with Advanced Cancer: When, Why, and How to Refer to Palliative Care Services
Bibliographic record
Abstract
Palliative care (pc) is a fundamental component of the cancer care trajectory. Its primary focus is on "the quality of life of people who have a life-threatening illness, and includes pain and symptom management, skilled psychosocial, emotional and spiritual support" to patients and loved ones. Palliative care includes, but is not limited to, end-of-life care. The benefits of early introduction of pc services in the care trajectory of patients with advanced cancer are well known, as indicated by improved quality of life, satisfaction with care, and a potential for increased survival. In turn, early referral of patients with advanced cancer to pc services is strongly recommended. So when, how, and why should patients with advanced cancer be referred to pc services? In this article, we summarize evidence to address these questions about early pc referral: ▪ What are the known benefits?▪ What is the "ideal" pc referral timing?▪ What are the barriers?▪ Which strategies can optimize integration of pc into oncology care?▪ Which communication tools can facilitate skillful introduction of pc to patients?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".