Predictors of Poor Outcomes in Critically Ill Adults with Hematologic Malignancy
Bibliographic record
Abstract
Background. Patients with hematologic malignancy (HM) often require intensive care unit (ICU) admission due to organ failure through disease progression or treatment-related complications. Objective. To determine mortality and prognostic variables in adult patients with HM who were admitted to ICU. Methods. Structured chart review of all adult patients (age ≥ 18 years) with HM admitted to ICU of a Canadian tertiary care hospital between 2004 and 2014. Outcome measures included mortality (ICU, 30-day, 60-day, and 12-month). Logistic regression was performed to determine predictors of mortality. Results. Overall, there were 206 cases of HM admitted to the ICU during the study (mean age: 51.3 ± 13.6 years; 60% male). Median stay was 3 days, with 14.1% requiring prolonged ICU admission. ICU mortality was 45.6% and increased to 59.2% at 30 days, 62.6% at 60 days, and 74.3% at 12 months. Predictors of increased ICU mortality included mechanical ventilation requirement and vasopressor therapy requirement, while admission to ICU postoperatively and having myeloma were associated with decreased mortality. Conclusions. Patients admitted to ICU with HM have high mortality (45.6%), which increased to 74.3% at 1 year. Analysis of multiple variables identified critical illness, postsurgical admission, and myeloma as predictors of patient outcomes.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".