Mortality in Patients with Leukemia and Lymphoma Urgently Admitted to the PICU: Secondary Analysis of Data from a Cluster Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Objectives were to describe the severity of illness in patients with leukemia or lymphoma urgently admitted to pediatric intensive care and explores the risk factors for mortality. A secondary analysis was performed of prospectively collected data from a cluster-randomized controlled trial in 21 children's hospitals from 2011 to 2015. Eligible patients were urgently admitted to intensive care and had a diagnosis of leukemia or lymphoma. Associations with intensive care mortality (primary outcome) were determined with multivariable generalized estimating equation with a logit link, accounting for clustering by site. Associations with time to intensive care mortality (secondary outcome) were determined with multivariable proportional hazards models. A total of 109 patients were included, age 115 (interquartile range [IQR] 42, 168) months and intensive care length of stay was 3 (IQR 2, 6) days. During the first hour in intensive care 36 (33%) were ventilated, and during intensive care 45 (41.3%) had at least 1 technology day. Day 1 Pediatric Logistic Organ Dysfunction (PELOD) score was ≥ 20 in 37 (33.9%), Pediatric Index of Mortality 2 mortality risk was > 10% in 35 (32.1%), and Children's Resuscitation Intensity Scale (RISC) was ≥ 3 (late admission to intensive care) in 32 (31.7%). Intensive care mortality was 20/109 (18.3%); with intensive care stay ≥ 20 days mortality was 51%. Previous urgent pediatric intensive care unit (PICU) admission, mechanical ventilation, and day 1 PELOD score were associated with higher PICU mortality. Mechanical ventilation, day 1 PELOD score, and late admission to the PICU (RISC ≥ 2) were associated with time to death. Patients with leukemia and lymphoma urgently admitted to intensive care had mortality of 18.3%, an improvement from historical cohorts. Risk factors were not accurate enough to make individual patient care decisions.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".