Timeliness of end-of-life (EOL) discussions for blood cancers: A national survey of hematologic oncologists.
Bibliographic record
Abstract
13 Background: Although timely EOL discussions have been shown to positively impact EOL care for patients with advanced solid tumors, little is known about EOL discussions for patients with blood cancers. Methods: In 2014, we mailed a 30-item survey to a national sample of hematologic oncologists randomly selected from the American Society of Hematology clinical directory. The survey was developed through focus groups (n=20) and cognitive debriefing (n=5) with hematologic oncologists. We report preliminary data regarding timing of EOL discussions. Results: We received 349 surveys from 48 states (response rate: 57.3%). Median age was 52 years, median time in practice was 25 years, and 43% practiced primarily in tertiary centers. Of all respondents, 56% reported that EOL discussions with blood cancer patients typically occur “too late.” The great majority also reported conducting initialdiscussions regarding resuscitation status, desire for hospice care, and preferred site of death at times other than periods of disease stability (Table). In multivariable analysis adjusting for gender, years in practice, and self-reported confidence leading EOL discussions, respondents practicing in tertiary centers were more likely to report that such discussions occur “too late” (OR=1.91, 95% CI [1.22, 2.98]). Similarly, hematologic oncologists practicing in tertiary centers were less likely to report conducting timely initial resuscitation status discussions (before acute hospitalization or before death clearly imminent, OR=0.52, 95% CI [0.33, 0.82]). Conclusions: The majority of hematologic oncologists in our large national cohort reported late EOL discussions. Moreover, clinicians in tertiary centers were more likely to report late discussions, even when prompted about specific EOL topics. Our data suggest that physician-focused interventions to improve timing of EOL discussions for blood cancers should target those practicing in tertiary centers. [Table: see text]
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".