Practice Patterns of Medical Oncologists: A Survey of Advance Care Planning in the Outpatient Setting
Bibliographic record
Abstract
Introduction . Advance care planning (ACP) is an important part of cancer care. We determined the ACP practice patterns of medical oncologists at our academic cancer centre in Canada. Methods . Medical oncologists were invited to participate in a questionnaire in August 2019. Questions were validated by a local survey expert. Twelve multiple‐choice questions were included. Results . Seventeen of the 23 eligible oncologists responded. 64% were male, and 76% were in practice for <16 years. Common tumour sites treated by respondents included breast (53%), lung (24%), gastrointestinal (24%), and genitourinary (24%) cancers. Oncologists responded that components of ACP included designating a substitute decision‐maker (100%), determining goals of care (100%), making decisions about cardiopulmonary resuscitation (94%), and disposition of property/finances (88%). They discuss ACP with curable vs. incurable patients 6% vs. 93% of the time. While 88% of oncologists reported it would be desirable to initiate ACP discussions in the first 3 visits, in the incurable setting, only 29% reported doing so. Patient characteristics that prompt oncologists to discuss ACP in the first 3 visits in the curative vs. incurable settings include elderly age (23% vs. 59%), poor performance status (47% vs. 88%), and short prognosis (47% vs. 88%). Oncologists thought the most appropriate time to discuss ACP in the curative setting was at the time the patient initiates it (35%), and during visits 2‐3 in the incurable setting (41%). The most common barriers to discussing ACP include insufficient time (71%) and too much information for the patient (71%). Conclusions . While medical oncologists believe that discussing ACP with cancer patients in the first few outpatient visits is important, this seldom occurs due to the presence of several barriers.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".