Severity of illness and organ dysfunction scoring in children
Bibliographic record
Abstract
OBJECTIVE: To describe predictive and descriptive general scores that can be used to estimate the severity of illness in critically ill children. DESIGN: Review of the medical literature. SETTING: Pediatric intensive care units (PICUs). PATIENTS: Critically ill children. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Two predictive scores are frequently used in PICUs: the Pediatric Risk of Mortality III score and the Pediatric Index of Mortality 2. The data considered in these scores are collected at baseline. Predictive scores can be used to compare expected and observed mortality in PICUs or to estimate the balance in the baseline severity of illness of patients included in the different arms of a randomized clinical trial. Only one descriptive score is validated to estimate the severity of cases of multiple organ dysfunction syndrome in PICUs, namely, the Pediatric Logistic Organ Dysfunction score. The data required to calculate this score are collected from baseline to discharge from the PICU or up to 2 hrs before death in the PICU. The Pediatric Logistic Organ Dysfunction score can be used to describe the clinical outcome of patients during their stay in a PICU. CONCLUSION: Pediatric Risk of Mortality III, Pediatric Index of Mortality 2, and Pediatric Logistic Organ Dysfunction scores are the best available tools to estimate the severity of illness in critically ill children.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".