Improving patient and caregiver outcomes in oncology: Team‐based, timely, and targeted palliative care
Bibliographic record
Abstract
Over the past decade, a large body of evidence has accumulated supporting the integration of palliative care into oncology practice for patients with advanced cancer. The question is no longer whether palliative care should be offered, but what is the optimal model of delivery, when is the ideal time to refer, who is in greatest need of a referral, and how much palliative care should oncologists themselves be providing. These questions are particularly relevant given the scarcity of palliative care resources internationally. In this state-of-the-science review directed at the practicing cancer clinician, the authors first discuss the contemporary literature examining the impact of specialist palliative care on various health outcomes. Then, conceptual models are provided to support team-based, timely, and targeted palliative care. Team-based palliative care allows the interdisciplinary members to address comprehensively the multidimensional care needs of patients and their caregivers. Timely palliative care, at its best, is preventive care to minimize crises at the end of life. Targeted palliative care involves identifying the patients most likely to benefit from specialist palliative care interventions, akin to the concept of targeted cancer therapies. Finally, the strengths and weaknesses of innovative care models, such as outpatient clinics, embedded clinics, nurse-led palliative care, primary palliative care provided by oncology teams, and automatic referral, are summarized. Moving forward, more research is needed to determine how different health systems can best personalize palliative care to provide the right level of intervention, for the right patient, in the right setting, at the right time. CA Cancer J Clin. 2018;680:00-00. 2018 American Cancer Society, Inc.
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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 | 0.007 | 0.021 |
| 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 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".