Predictors of triage pain assessment and subsequent pain management among pediatric patients presenting to the emergency department
Bibliographic record
Abstract
BACKGROUND: Pediatric patients with pain of various causes present to the emergency department. Appropriate assessment and management of pain are important aspects of emergency department treatment. However, only a few studies have identified the predictors of both outcomes. This study aimed to evaluate the rate of pain assessment at triage and subsequent management and to identify the predictors of each outcome. METHODS: This was a multi-center retrospective study based at five community emergency departments. Pediatric patients (< 18 years) with pain or injury who presented to the emergency department between February 2018 and May 2018 were included. In addition to patient demographics, the initial pain assessment at triage, reason for visit, and time to analgesia were determined. Further, the type and route of analgesia were identified in patients who received analgesia. Univariate and multivariable regression models were used to identify predictors of pain assessment and management. RESULTS: There were 4,128 patients with an average age of 9.6 years, and 49.1% of them were female. Only 74.2% of the patients underwent assessment for pain at triage, and 18.3% received analgesia. The median time to analgesia was 95 (IQR: 49-154) min. Most patients presented with head/neck (36.1%), upper limb (21.6%), and lower limb (19.9%) pain. The oral route was the most common analgesia delivery method (67.4%), and ibuprofen and acetaminophen were the primary agents used. Younger age, higher acuity, and presenting with head or neck pain were independent predictors of pain assessment at triage, while children 3-5 years and those with lower extremity pain were more likely to receive analgesia. CONCLUSION: Although pain assessment at triage has improved in pediatric patients, there is still a major deficiency in adequate pain management. Our study highlights predictors of pain assessment and management that can be considered for improved pediatric care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".