Does prognostic uncertainty affect discussions of prognosis? Lessons from a survey of hematologic oncologists.
Bibliographic record
Abstract
45 Background: Although recent advances in cancer therapy have improved survival for patients with solid tumors, they have also increased the complexity of prognostication (Temel, JCO 2016). Prognostic uncertainty is particularly prevalent in hematologic oncology (LeBlanc, JOP 2014) and potentially a barrier to timely end-of-life (EOL) communication (Odejide, JCO 2016). Methods: In 2015, we mailed a 30-item survey to a national sample of hematologic oncologists randomly selected from the American Society of Hematology directory. The survey was developed through focus groups (n = 20) and cognitive debriefing (n = 5). We aimed to characterize respondents’ reports of prognostic discussions, as well as their timeliness and content. Results: We received 349 surveys from 48 states (response rate: 57%). Median time in practice was 25 years and 57% practiced in community settings. Overall, 60% reported discussing prognosis with “most” ( > 95%) of their patients. Those with < 15 years clinical experience (AOR = 0.54, 95% CI 0.31, 0.94) and those considering prognostic uncertainty to be a barrier to EOL care (AOR = 0.57, 95% CI 0.35, 0.92) were less likely to have prognostic discussions with “most” of their patients. When discussing prognosis, almost all (98%) reported typically having an initial discussion at diagnosis or during a period of stability; however, 18% reported either never readdressing prognosis or doing so only when death is clearly imminent. In terms of preferred terminology, 57% reported routinely having “general discussions of potentially curable disease,” while 43% preferred providing specific data such as percent chance of survival or median survival. Conclusions: The majority of hematologic oncologists in this large cohort reported discussing prognosis with their patients, but doing so qualitatively, focusing on whether cure is possible. About one-fifth reported not readdressing prognosis in a timely manner. These suggest that the prognostic uncertainty common with blood cancers fosters missed opportunities to convey what is known about prognosis. Given the growing difficulty in solid tumor prognostication, these data may foreshadow coming communication gaps for oncology as a whole.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".