Reduce Mortality and Morbidity in Acute Myeloid Leukemia With Hyperleukocytosis With Early Admission in Intensive Care Unit: A Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Patients presenting with acute myeloid leukemia (AML) at diagnosis are at high risk of severe complications and death, particularly with high white blood cell (WBC) count. In this retrospective study, we evaluate interest of early and systematic support in the intensive care unit (ICU) for AML with hyperleukocytosis (AML-HL) at diagnosis. METHODS: /L, primary referred in ICU ("Early ICU") without organ failure and before initiating chemotherapy induction were compared to patients first admitted in the Hematology Department who required a secondary transfer in ICU ("Late ICU") or not ("No ICU"). Primary end point was mortality during the first month, and secondary end points were the use of life-sustaining therapies in ICU and risk factors for ICU transfer and mortality. RESULTS: One hundred fifty-four patients were included: 77 (50%) to the group "No ICU", 18 (12%) to "Late ICU" and 59 (38%) to "Early ICU". Mortality at day 30 was higher in "Late ICU" than in "Early ICU" and "No ICU", with 27.8%; 16.9% and 2.6% respectively (P < 0.001). "Late ICU" patients had an increased use of life-sustaining therapy comparing to "Early ICU" patients (56% vs. 29%, P = 0.04). CONCLUSIONS: Early referral to ICU reduces morbidity and seems an effective strategy to reduce short-term mortality in AML-HL at diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".