The Association of Corticosteroids and Pediatric Sepsis Biomarker Risk Model (PERSEVERE)-II Biomarker Risk Stratification With Mortality in Pediatric Septic Shock*
Bibliographic record
Abstract
OBJECTIVES: Mortality risk stratification may identify a subset of children who benefit from or are harmed by corticosteroid administration. The Pediatric Sepsis Biomarker Risk Model (PERSEVERE)-II score is a biomarker-based mortality risk stratification tool for pediatric sepsis. Our objective was to assess the association of corticosteroid administration with 28-day mortality within different levels of baseline mortality risk (PERSEVERE-II) in a cohort of children with septic shock. DESIGN: We performed a secondary analysis using prospectively collected data (January 2015 to December 2018). SETTING: PICUs in 13 tertiary care, academic centers in the United States. PATIENTS: Children with septic shock. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We assessed the association of corticosteroid administration within PERSEVERE-II risk score categories and 28-day mortality, ICU-free days, and maximum failed organs in children with septic shock. We analyzed a total of 461 patients (215 with corticosteroids exposure, 246 without corticosteroid exposure) with an average age of 7.1 years (interquartile range, 2.2-13.6 yr). In the subgroup of patients with a high PERSEVERE-II score, corticosteroid administration was associated with an increased adjusted risk of 28-day mortality (odds ratio [OR] 4.10 [95% CI 1.70-9.86]; p = 0.002), but not in the low risk group (OR 0.20 [95% CI 0.02-1.73]; p = 0.15). A significant interaction between PERSEVERE-II score and corticosteroids was seen for both secondary outcomes complicated course ( p = 0.01) and maximum failed organs ( p < 0.001). Corticosteroid exposure was associated with fewer ICU-free days ( p < 0.0001). CONCLUSIONS: In our multicenter observational study, corticosteroid administration was associated with increased mortality in a subgroup of children with a high PERSEVERE-II risk score.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".