Factors Associated With Improved Pediatric Resuscitative Care in General Emergency Departments
Bibliographic record
Abstract
OBJECTIVES: To describe the quality of pediatric resuscitative care in general emergency departments (GEDs) and to determine hospital-level factors associated with higher quality. METHODS: Prospective observational study of resuscitative care provided to 3 in situ simulated patients (infant seizure, infant sepsis, and child cardiac arrest) by interprofessional GED teams. A composite quality score (CQS) was measured and the association of this score with modifiable and nonmodifiable hospital-level factors was explored. RESULTS: A median CQS of 62.8 of 100 (interquartile range 50.5-71.1) was noted for 287 resuscitation teams from 175 emergency departments. In the unadjusted analyses, a higher score was associated with the modifiable factor of an affiliation with a pediatric academic medical center (PAMC) and the nonmodifiable factors of higher pediatric volume and location in the Northeast and Midwest. In the adjusted analyses, a higher CQS was associated with modifiable factors of an affiliation with a PAMC and the designation of both a nurse and physician pediatric emergency care coordinator, and nonmodifiable factors of higher pediatric volume and location in the Northeast and Midwest. A weak correlation was noted between quality and pediatric readiness scores. CONCLUSIONS: A low quality of pediatric resuscitative care, measured using simulation, was noted across a cohort of GEDs. Hospital factors associated with higher quality included: an affiliation with a PAMC, designation of a pediatric emergency care coordinator, higher pediatric volume, and geographic location. A weak correlation was noted between quality and pediatric readiness scores.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".