Abstract PD-013: EXTERNAL VALIDATION OF THE “QUICK” PEDIATRIC LOGISTIC ORGAN DYSFUNCTION-2 SCORE USING A LARGE NORTH AMERICAN COHORT OF CRITICALLY ILL CHILDREN
Bibliographic record
Abstract
Aims & Objectives: Early detection of sepsis is important to decrease its substantial mortality [PMID: 25734408]. A quick Pediatric Logistic Organ Dysfunction-2 score on day 1 (qPELOD2) [28492402] was useful in predicting mortality with an area under receiver operating characteristic curve (AUC) of 0.91 (95%CI 0.86–0.96) in children admitted to a PICU with suspected infection [28492402]. We performed an external validation of the qPELOD2 score. Methods A secondary analysis was performed using data from the Virtual Pediatric Systems registry of records from 130 PICUs in North America. Children with an infectious diagnosis code admitted between January 2009 and December 2014 were included. Systolic blood pressures, heart rates, and Glasgow coma scores were used to evaluate the qPELOD2 score [28492402]. Performance was compared with pediatric risk of mortality III (PRISM3) [8706448] and pediatric index of mortality 2 (PIM2) risk scores [12541154]. Results Data from 42,196 children, of median age 2.7 (IQR 0.7–8.8, range 0–18) years and with a 4.27% mortality rate, were analyzed. Mortality was 13.4% with a qPELOD2 ≥2, and 2.5% for qPELOD2 <2. The AUC of qPELOD2 was 72.6 (95%CI 71.4−73.8) (Figure 1). Performance of the qPELOD2 was worst in the >12 years age group: AUC 67.8 (95% CI 65−70.5), and best in the <1 month age group: AUC 78.9 (95%CI 75.3−82.4) (Figure 2).Conclusions qPELOD2 performed significantly poorer in our cohort, compared to the original study. Further work is needed to needed to discern the reason and to develop a robust quick pediatric sepsis diagnostic tool for both research and clinical care.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".