Multiple Interventions Improve Analgesic Treatment of Supracondylar Fractures in a Pediatric Emergency Department
Bibliographic record
Abstract
BACKGROUND: Provision of appropriate and timely treatment for pain in the pediatric population has been challenging. Children with painful conditions commonly present to emergency departments (EDs), a setting in which it may be particularly difficult to consistently provide timely analgesic interventions. OBJECTIVES: To measure the effectiveness of a set of interventions in improving the rate and timeliness of analgesic medication administration, as well as appropriate backslab immobilization (application of a moldable plaster or fiberglass splint), in a pediatric ED. METHODS: Data regarding pain management were collected on a consecutive sample of cases of supracondylar fracture over a 13-month period. This followed the implementation of a formal triage pain assessment and treatment medical directive, supplemented with relevant education of nursing and house staff, and posters in the ED. These data were compared with data previously collected from a similar cohort of cases, which presented before the interventions. RESULTS: Postintervention, the proportion of patients treated with an analgesic within 60 min of triage increased from 15% to 54% (P<0.001), and the median time to administration of an analgesic decreased from 72.5 min to 11 min (P<0.001). Rates for backslab application before radiography were similar before and after the intervention (29% and 33%, respectively; P=0.646). CONCLUSIONS: A multifaceted approach to improving early analgesic interventions was associated with considerably improved rates of early analgesic treatments for supracondylar fracture; however, no improvement in early immobilization was observed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".