Evidence-Informed Quality Indicators for Pediatric Trauma Care
Bibliographic record
Abstract
Importance: Despite the unique physiological characteristics and health care needs of pediatric trauma patients, there is a lack of quality indicators (QIs) based on pediatric-specific evidence to support quality improvement in this population. Objective: To develop a consensus-based set of QIs for acute pediatric trauma care that considers evidence on effectiveness, safety, cost-effectiveness, equity, and caregiver perspectives and is applicable in pediatric and nonpediatric trauma centers. Design, Setting, and Participants: A modified Research and Development (RAND)/University of California Los Angeles (UCLA) expert consensus study was conducted consisting of an online survey and a virtual workshop, led by an independent moderator. Panelists represented key areas of pediatric trauma patient management, diverse care settings (from level I pediatric trauma centers to level III referring centers), 5 high-resource countries, and caregivers. Data were analyzed from May to August 2024. Exposure: Likert-scale ratings of 41 QIs. Main Outcomes and Measures: Panelists rated 41 QIs on a 7-point Likert scale according to 4 criteria: importance, supporting evidence, actionability, and measurability. QIs with a global score of 24 of 28 or greater and an importance score of 6 of 7 or greater were considered accepted by consensus. Results: A total of 65 experts were invited, of whom 59 accepted (91%; 25 over 50 years of age [44.7%]; 34 female [60.7%]), 56 (95%) completed the first round, and 54 (92%) completed both rounds. Twenty-three QIs were selected covering key areas of acute pediatric trauma management (eg, transfer to a pediatric trauma center for neurotrauma or major multisystem trauma, documentation of vital signs, early rehabilitation, nutritional support), the most common types of injuries (eg, hypertonic saline in severe traumatic brain injury, stabilization of femoral shaft fractures, nonoperative management of solid organ injuries), value in care (eg, imaging in children at low risk on a clinical decision rule), patient-centered care (eg, designated support person, caregiver presence), and equity (eg, mental health screening). Conclusions: These results may be used by trauma quality improvement programs in high-resource countries to select context-specific quality indicators to improve the effectiveness, safety, cost-effectiveness, equity, and patient-centered nature of pediatric trauma 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 | 0.309 | 0.513 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".