Quality Indicators for High Acuity Pediatric Conditions
Bibliographic record
Abstract
OBJECTIVE: Identifying gaps in care and improving outcomes for severely ill children requires the development of evidence-based performance measures. We used a systematic process involving multiple stakeholders to identify and develop evidence-based quality indicators for high acuity pediatric conditions relevant to any emergency department (ED) setting where children are seen. METHODS: A prioritized list of clinical conditions was selected by an advisory panel. A systematic review of the literature was conducted to identify existing indicators, as well as guidelines and evidence that could be used to inform the creation of new indicators. A multiphase, Rand-modified Delphi method consisting of anonymous questionnaires and a face-to-face meeting of an expert panel was used for indicator selection. Measure specifications and evidence grading were created for each indicator, and the feasibility and reliability of measurement was assessed in a tertiary care pediatric ED. RESULTS: The conditions selected for indicator development were diabetic ketoacidosis, status asthmaticus, anaphylaxis, status epilepticus, severe head injury, and sepsis. The majority of the 62 selected indicators reflect ED processes (84%) with few indicators reflecting structures (11%) or outcomes (5%). Thirty-seven percent (n = 23) of the selected indicators are based on moderate or high quality evidence. Data were available and interrater reliability acceptable for the majority of indicators. CONCLUSIONS: A systematic process involving multiple stakeholders was used to develop evidence-based quality indicators for high acuity pediatric conditions. Future work will test the reliability and feasibility of data collection on these indicators across the spectrum of ED settings that provide care for children.
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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 | 0.138 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".