A Retrospective Case-Control Study to Identify Predictors of Unplanned Admission to Pediatric Intensive Care Within 24 Hours of Hospitalization*
Bibliographic record
Abstract
OBJECTIVES: To identify the clinical findings available at the time of hospitalization from the emergency department that are associated with deterioration within 24 hours. DESIGN: A retrospective case-control study. SETTING: A pediatric hospital in Ottawa, ON, Canada. PATIENTS: Children less than 18 years old who were hospitalized via the emergency department between January 1, 2008, and December 31, 2012. Cases (n = 98) had an unplanned admission to the PICU or unexpected death on the hospital ward within 24 hours of hospitalization and controls (n = 196) did not. INTERVENTIONS: None. MAIN RESULTS: Ninety-eight children (53% boys; mean age 63.2 mo) required early unplanned admission to the PICU. Multivariable conditional logistic regression resulted in a model with five predictors reaching statistical significance: higher triage acuity score (odds ratio, 4.1; 95% CI, 1.7-10.2), tachypnea in the emergency department (odds ratio, 4.6; 95% CI, 1.8-11.8), tachycardia in the emergency department (odds ratio, 2.6; 95% CI, 1.1-6.5), PICU consultation in the emergency department (odds ratio, 8.0; 95% CI, 1.1-57.7), and admission to a ward not typical for age and/or diagnosis (odds ratio, 4.5; 95% CI, 1.7-11.6). CONCLUSIONS: We have identified risk factors that should be included as potential predictor variables in future large, prospective studies to derive and validate a weighted scoring system to identify hospitalized children at high risk of early clinical deterioration.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".