Intensivist and Hematologist Perceptions of Prognosis of Critically Ill Patients with Hematologic Malignancies
Bibliographic record
Abstract
Objectives Historically, patients with hematologic malignancies were often declined ICU admission due to anticipated poor outcomes. However, recent publications describe significant improvements in ICU and in-hospital mortality for critically ill patients with hematologic malignancies. It is unclear whether clinicians’ perceptions of outcomes in this patient population have changed, or whether there is consensus on management. This study evaluated intensivist and hematologist perceptions of prognosis in critically ill patients with hematologic malignancies and identified factors that inform their decision-making. Design We conducted an electronic cross-sectional survey of Canadian intensivists and hematologists. The survey included 19 questions and a case-based scenario with variations in clinical factors. The survey data were summarized using frequency with percent. Data was compared between intensivists and hematologists using χ 2 tests for categorical data. A post-hoc analysis of secondary variables was also conducted using χ 2 tests. Results A total of 180 clinicians responded to the survey - 63% were intensivists, 36% hematologists and 1% dually trained. Most clinicians reported using a variety of cancer-, patient- and critical illness-related factors for prognostication, and most demonstrated awareness of factors associated with worse prognosis in this patient population. When presented with a hypothetical case, survey results revealed consensus on admitting the patient to ICU but variability in limitations to treatment and goals of care. Additionally, we found wide variability in predicted patient outcomes. There was significant variability in decision-making around withdrawal of life sustaining therapies, but minimal between-group differences between intensivist and hematologist responses. Conclusions This study found significant variation among clinicians in predicting prognosis for critically ill patients with hematologic malignancies, although concordance between intensivists and hematologists overall. Further study examining factors affecting prognosis and long-term outcomes for this patient population will help guide clinicians and better inform decisions about medical care.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".