Perspectives on Advance Care Planning for Patients with Hematologic Malignancy: An International Clinician Questionnaire
Bibliographic record
Abstract
Abstract Rationale Critical illness is common in patients with hematologic malignancy (HM). Advance care planning (ACP) can allow these patients to express their care preferences before life-threatening illnesses. Objectives To evaluate physicians’ perspectives surrounding ACP in patients with HM. Methods We administered a survey to intensivists and hematologic oncologists who care for patients with HM across Canada and the United Kingdom. Potential respondents were identified from institutions that have a hematologic-oncology program. The survey was disseminated electronically. Results A total of 111 physicians completed the survey, with a response rate of 19% (39% across those who opened the e-mail); 52% of respondents were intensivists, and 48% of respondents were hematologic oncologists. Of the responses, 15.5% of physicians reported that ACP happens routinely at their institution, whereas 8.3% of physicians stated that code status is routinely discussed. ACP discussions were most commonly reported at the onset of critical illness (84.3% of respondents), during disease recurrence (52.9% of respondents), or during the transition to a strictly palliative approach (54.9% of respondents). Commonly cited barriers to ACP centered on physicians’ concern about the reaction of the patient or family. Conclusions This study emphasizes the need for earlier and more frequent ACP discussions in this high-risk population with a variety of barriers identified.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".