Integrating Palliative Care and Hematologic Malignancies: Bridging the Gaps for Our Patients and Their Caregivers
Bibliographic record
Abstract
Patients with hematologic malignancies (HMs) struggle with immense physical and psychological symptom burden, which negatively affect their quality of life (QOL) throughout the continuum of illness. These patients are often faced with substantial prognostic uncertainty as they navigate their illness course, which further complicates their medical decision making, especially at the end of life (EOL). Consequently, patients with HM often endure intensive medical care at the EOL, including frequent hospitalization and intensive care unit admissions, and they often die in the hospital. Our EOL health care delivery models are not well suited to meet the unique needs of patients with HMs. Although studies have established the role of specialty palliative care for improving QOL and EOL outcomes in patients with solid tumors, numerous disease-, clinician-, and system-based barriers prevail, limiting the integration of palliative care for patients with HMs. Nonetheless, multiple studies have emerged over the past decade identifying the role of palliative care integration in patients with various HMs, resulting in improvements in patient-reported QOL, symptom burden, and psychological distress, as well as EOL care. Importantly, these studies have also identified active components of specialty palliative care interventions, including strategies to promote adaptive coping especially in the face of prognostic uncertainty. Future work can leverage the knowledge gained from specialty palliative care integration to develop and test primary palliative care interventions by training clinicians caring for patients with HMs to incorporate these strategies into their clinical practice.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".