Outcome and Predictors of Mortality in Pediatric Oncology Patients Requiring Intensive Care
Bibliographic record
Abstract
Children with malignancies admitted to pediatric intensive care units (PICUs) for complications of their treatment regimes have a high mortality. There are seven previous publications addressing the outcome of this group of patients. We retrospectively analyzed data from 171 admissions (134 patients) to the PICU for predictors of outcome. The most frequent underlying diagnosis was acute lymphoblastic leukemia (ALL) (n = 23, 17%) followed by post-bone marrow transplant (DMT) (n = 12, 9%), but a wide variety of tumors were represented. Sixty-three children were admitted with suspected sepsis, 58 with positive cultures. This represents the largest series published. Mortality for sepsis was 53% and for systemic inflammatory response syndrome (SIRS) (negative cultures), 80%. Logistic regression analysis revealed the number of organs failed, day 1 PRISM score, and the dose and type of inotropes required to be independent predictors of mortality. The need for ventilation alone was not an independent predictor of death. Twenty-six percent of all ventilated children with sepsis survived, with a mean number of failed organs in the survivors of 1.8. Children with respiratory failure without multiple organ dysfunction syndrome (MODS) have a significant chance of survival. Mortality was 100% for four or more failed organs and was 92% for children in renal failure. Survival if ventilation and inotropes were required was 9.4%, higher than expected.
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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 | 0.004 |
| 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 | 0.001 |
| 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".