The utilization of pain assessment tools in pediatric emergency for better pain management
Bibliographic record
Abstract
Objective: Accurate assessment of acute pain in children is essential for effective emergency care but can be challenging due to varying pain expressions across ages. Our study aims to examine healthcare providers’ efforts to enhance assessment using age-appropriate tools. Methods: Patients were retrospectively selected from the King Abdulaziz University Hospital Emergency records which involved a cohort review of 157 children presented to the pediatric emergency department with acute pain from 2017 to 2018. Routine pain assessment tool grading acute pain as mild, moderate, severe by qualified pediatric emergency doctors, Canadian triage acuity scale (CTAS) and numerical rating scale (NRS) were used to describe pain intensity. Inter-statistical cohort analysis was used. Results: The mean age of patients were 8±3.3 years (range: 2.5-13.9 years) with 73 girls and 84 boys. About 80% (n=126) of the children presented to the emergency department with acute pain were scored as CTAS 2-3. All triaged patients passed to the emergency department were assessed as mild (n=66, 42%), moderate (n=27, 17%) and severe (n=35, 22%) pain. The NRS scoring was used in only 12 (7.6%) children as NRS only applies to older children. Paracetamol and nonsteroidal anti-inflammatory drug (NSAID) were the most frequent analgesia administered by the health care providers. There was a statistically insignificant relationship between the severity of the pain and the type of analgesia (P value>0.05). Children with mild pain had a significantly higher level of NSAID administration than those with moderate or severe pain (P<0.05). Children with mild pain had a significantly higher level of NSAID administration than those with moderate or severe pain (P<0.05). Conclusion: Pain assessment with scoring methods like CTAS or NRS in the emergency room (ER) is crucial despite challenges. Inconsistent use affects outcomes, emphasizing the need for research to encourage consistent application in pediatric emergency care.
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How this classification was reachedexpand
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".