Palliative and end-of-life care for the older adult with cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite established benefits of palliative care in the oncology population, it remains an underutilized resource particularly among older adults. The illness trajectory and needs of an older adult with cancer are unique. The purpose of this paper is to review the current literature on providing comprehensive palliative and end-of-life care for the older adult with cancer. RECENT FINDINGS: Though the difficulties of applying traditional palliative care principles in the older patients with cancer have been discussed, this review reveals a clear gap in the literature in discussing the provision of comprehensive palliative and end-of-life care in this population. Very few articles have been published in this domain with even fewer published within the past 18 months. SUMMARY: As such, this article reviews key aspects of palliative and geriatric medicine that need to be considered and integrated in order to provide comprehensive palliative care to the older adult with cancer. This includes a discussion of proper pain and symptoms assessment, performance status assessment, advance care planning, and end-of-life care while considering the nuances of geriatric syndromes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".