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Record W1967706956 · doi:10.1542/peds.2005-3029

Development of Measures of the Quality of Emergency Department Care for Children Using a Structured Panel Process

2006· article· en· W1967706956 on OpenAlexafffundabout
Astrid Guttmann, Asma Razzaq, Patty Lindsay, Brandon Zagorski, Geoffrey M. Anderson

Bibliographic record

VenuePEDIATRICS · 2006
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersHealth Canada
KeywordsMedicineEmergency departmentPediatric emergency medicineTriageMedical emergencyBronchiolitisEmergency medicineNursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Performance measures are essential components of public reporting and quality improvement. To date, few such measures exist to provide a comprehensive assessment of the quality of emergency department services for children. OBJECTIVES: Our goal was to use a systematic process to develop measures of emergency department care for children (0-19 years) that are (1) based on research evidence and expert opinion, (2) representative of a range of conditions treated in most emergency departments, (3) related to links between processes and outcomes, and (4) feasible to measure. METHODS: We presented a panel of providers and managers data from emergency department use to identify common conditions across levels of patient acuity, which could be targets for quality improvement. We used a structured panel process informed by a literature review to (1) identify condition-specific links between processes of care and defined outcomes and (2) select indicators to assess these process-outcome links. We determined the feasibility of calculating these indicators using an administrative data set of emergency department visits for Ontario, Canada. RESULTS: The panel identified 18 clinical conditions for indicator development and 61 condition-specific links between processes of care and outcomes. After 2 rounds of ratings, the panel defined 68 specific clinical indicators for the following conditions: adolescent mental health problems, ankle injury, asthma, bronchiolitis, croup, diabetes, fever, gastroenteritis, minor head injury, neonatal jaundice, seizures, and urinary tract infections. Visits for these conditions account for 23% of all pediatric emergency department use. Using an administrative data set, we were able to calculate 19 indicators, covering 9 conditions, representing 20% of all emergency department visits by children. CONCLUSIONS: Using a structured panel process, data on emergency department use, and literature review, it was possible to define indicators of emergency department care for children. The feasibility of these indicators will depend on the availability of high-quality data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.346
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations112
Published2006
Admission routes3
Has abstractyes

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