Understanding challenges and barriers to quality end-of-life care for patients with hematologic malignancies: a GIMEMA survey
Bibliographic record
Abstract
Patients with hematologic malignancies often receive aggressive end-of-life (EOL) care, which may be partly related to hematologists' discomfort with discontinuing aggressive treatments at EOL. It is therefore important to investigate how hematologists perceive EOL care and how this affects their clinical practice. We assessed a cohort of Italian hematological oncologists through a GIMEMA online survey to explore their attitudes toward standard measures of quality EOL care, their opinions on barriers to providing this care, and potential interventions. EOL quality measures were defined acceptable to hematologist if at least 55% of respondents agreed with their suitability. One-hundred eight-six hematologists completed the survey. Hematologists rated 8 of 13 EOL quality measures as highly acceptable, including no new chemotherapy, no intensive care unit admission, no intubation/cardiopulmonary resuscitation in the last 30 days of life, and hospice admission > 7 days before death. Major barriers to quality EOL care included unrealistic patient expectations, clinician concerns about taking away hope, and uncertainty about what to say. Moreover, 73% admitted to being unfamiliar with discussing goals of care (GOC) or advance care planning (ACP). Suggested interventions for improvement included increasing the availability and timely integration of palliative care, and access to home care services. In conclusion, Italian hematologists find most standard EOL quality measures acceptable, they identify barriers to quality care, and are open to interventions, including early integration of palliative care, to improve patients' EOL care. However, they lack familiarity with GOC and ACP discussions, highlighting the need for communication skills training.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".