How Do Blood Cancer Doctors Discuss Prognosis? Findings from a National Survey of Hematologic Oncologists
Bibliographic record
Abstract
Abstract Background: Although blood cancers are accompanied by a high level of prognostic uncertainty, little is known about when and how hematologic oncologists discuss prognosis. Objectives: Characterize reported practices and predictors of prognostic discussions for a cohort of hematologic oncologists. Design: Cross-sectional mailed survey in 2015. Setting/Subjects: U.S.-based hematologic oncologists providing clinical care for adult patients with blood cancers. Measurements: We conducted univariable and multivariable analyses assessing the association of clinician characteristics with reported frequency of initiation of prognostic discussions, type of terminology used, and whether prognosis is readdressed. Results: We received 349 surveys (response rate = 57.3%). The majority of respondents (60.3%) reported conducting prognostic discussions with “most” (>95%) of their patients. More than half (56.8%) preferred general/qualitative rather than specific/numeric terms when discussing prognosis. Although 91.3% reported that they typically first initiate prognostic discussions at diagnosis, 17.7% reported routinely never readdressing prognosis or waiting until death is imminent to revisit the topic. Hematologic oncologists with ≤15 years since medical school graduation (odds ratio [OR] 0.51; confidence interval (95% CI) 0.30–0.88) and those who considered prognostic uncertainty a barrier to quality end-of-life care (OR 0.57; 95% CI 0.35–0.90) had significantly lower odds of discussing prognosis with “most” patients. Conclusions: Although the majority of hematologic oncologists reported discussing prognosis with their patients, most prefer general/qualitative terms. Moreover, even though prognosis evolves during the disease course, nearly one in five reported never readdressing prognosis or only doing so near death. These findings suggest the need for structured interventions to improve prognostic communication for patients with blood cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".