Severity of illness and organ dysfunction scoring in children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it