The Association Between Pediatric Readiness and Mortality for Injured Children Treated at US Trauma Centers
Bibliographic record
Abstract
OBJECTIVE: To use updated 2021 weighted Pediatric Readiness Score (wPRS) data to identify a threshold level of trauma center emergency department (ED) pediatric readiness. BACKGROUND: Most children in the United States receive initial trauma care at nonpediatric centers. The aim of the National Pediatric Readiness Project (NPRP) was to ensure that all EDs are prepared to provide quality care for children. Trauma centers reporting the highest quartile of wPRS on the 2013 national assessment have been shown to have lower mortality. Significant efforts have been invested to improve pediatric readiness in the past decade. STUDY DESIGN: A retrospective cohort of trauma centers that completed the NPRP 2021 national assessment and contributed to the National Trauma Data Bank (NTDB) from 2019 to 2021 was analyzed. Center-specific observed-to-expected mortality estimates for children (0-15 y) were calculated using Pediatric Trauma Quality Improvement Program models. Deterministic linkage was used for transferred patients to account for wPRS at the initial receiving center. Center-specific mortality odds ratios were then compared across quartiles of wPRS. RESULTS: A total of 66,588 children from 630 centers with a median (interquartile range) wPRS of 79 (66-93) were analyzed. The average observed-to-expected odds of mortality [1.02 (0.97-1.06)] for centers in the highest quartile (wPRS≥93) was lower than any of the lowest 3 wPRS quartiles [1.19 (1.14-1.23) (Q1), 1.29 (1.24-1.33) (Q2), and 1.28 (1.19-1.36) (Q3), all P <0.05). The presence of a pediatric-specific quality improvement plan was the domain with the strongest independent association with mortality [standardized beta -0.095 (-0.146 to -0.044)]. CONCLUSION: Trauma centers should address gaps in pediatric readiness to include a pediatric-specific quality improvement plan and aim to achieve wPRS ≥93.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".