Patient-Reported Pain Outcomes for Children Attending an Emergency Department With Limb Injury
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to describe patient-reported pain outcomes at various stages of an emergency department (ED) visit for pediatric limb injury. METHODS: This prospective cohort consisted of 905 patients aged 4 to 17 years with acute limb injury and a minimum initial pain score of 4/10. Patients reported pain scores and treatments offered and received at each stage of their ED visit. Multiple logistic regression was used to identify predictors for severe pain on initial assessment and moderate or severe pain at ED discharge. RESULTS: The initial median pain score was 6/10 (interquartile range, 4-6) and decreased at discharge to 4/10 (interquartile range, 2-6). Stages of the ED visit where the highest proportion of patients reported severe pain (score, ≥8 of 10) were fracture reduction (26.0% [19/73]; 95% confidence interval [CI], 17.1%-37.5%), intravenous insertion (24.4% [11/45]; 95% CI, 13.8%-39.6%), and x-ray (23.7% [158/668]; 95% CI, 20.6%-27.0%). Predictors of severe pain at initial assessment included younger age (odds ratio [OR], 0.92; 95% CI, 0.87-0.97), female sex (OR, 0.58; 95% CI, 0.40-0.84), and presence of fracture (OR, 1.58; 95% CI, 1.07-2.33) whereas, at discharge, older age (OR, 1.14; 95% CI, 1.06-1.23) predicted moderate/severe pain (score, ≥4 of 10). CONCLUSIONS: These results on the location and predictors of severe pain during an ED visit for limb injury can be used to target interventions to improve pain management and patient outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".