Characteristics and Timing of Mortality in Children Dying With Infections in North American PICUs*
Bibliographic record
Abstract
OBJECTIVES: To investigate the characteristics and timing of death of children with severe infections who die during PICU admission. DESIGN: We analyzed demographics, timing of death, diagnoses, and common procedures in a large cohort obtained from the Virtual Pediatrics Systems database, focusing on early deaths (< 1 d). SETTING: Clinical records were prospectively collected in 130 PICUs across North America. PATIENTS: Children admitted between January 2009 and December 2014 with at least one infection-related diagnosis at time of death. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Analysis included data from 106,464 children admitted to PICUs. The 4,240 children (4%) who died were older than PICU survivors. The median (interquartile range) duration in PICU prior to death was 7.1 days (2.1-21.3 d), with 635 children (15%) dying early (< 1 d of PICU admission). Children who died early were older, more likely to have septic shock, and more likely to have received cardiopulmonary resuscitation than those who died later. Withdrawal of care was less likely in early deaths compared with later deaths. After adjusting for age, sex, sepsis severity, procedures (including cardiopulmonary resuscitation and heart, lung, and renal support), and number of admissions contributed per PICU, it was found that children admitted from the emergency department, inpatient floors, or referring hospitals had significantly greater risk of early death compared with children admitted from the operating room. CONCLUSIONS: A substantial proportion of children admitted to PICU with severe infections die early and differ from those dying later in diagnoses, procedures, and admitting location. The emergency department is a key source of critically ill patients. Understanding characteristics of early deaths may yield recruitment considerations for clinical trials enrolling children at high risk of early death.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".