Quality Indicators for the Assessment and Management of Pain in the Emergency Department: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Evidence indicates that pain is undertreated in the emergency department (ED). The first step in improving the pain experience for ED patients is to accurately and systematically assess the actual care being provided. Identifying gaps in the assessment and treatment of pain and improving patient outcomes requires relevant, evidence-based performance measures. OBJECTIVE: To systematically review the literature and identify quality indicators specific to the assessment and management of pain in the ED. METHODS: Four major bibliographical databases were searched from January 1980 to December 2010, and relevant journals and conference proceedings were manually searched. Original research that described the development or collection of data on one or more quality indicators relevant to the assessment or management of pain in the ED was included. RESULTS: The search identified 18,078 citations. Twenty-three articles were included: 15 observational (cohort) studies; three before-after studies; three audits; one quality indicator development study; and one survey. Methodological quality was moderate, with weaknesses in the reporting of study design and methodology. Twenty unique indicators were identified, with the majority (16 of 20) measuring care processes. Overall, 91% (21 of 23) of the studies reported indicators for the assessment or management of presenting pain, as opposed to procedural pain. Three of the studies included children; however, none of the indicators were developed specifically for a pediatric population. CONCLUSION: Gaps in the existing literature include a lack of measures reflecting procedural pain, patient outcomes and the pediatric population. Future efforts should focus on developing indicators specific to these key areas.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.112 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it