Predictors for Opioid Analgesia Administration in Children With Abdominal Pain Presenting to the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Abdominal pain is one of the most common symptoms in children. The aim of this study was to determine the rate of opioid analgesia in children with abdominal pain presenting to the pediatric Emergency Department (ED) and to identify factors associated with administration of opioids. METHODS: We retrospectively reviewed all charts of patients with abdominal pain < 7 days presenting to the ED of a tertiary pediatric hospital over a 3-month period. Demographic and illness-related variables were recorded, and the primary outcome variable was whether opioid analgesia was used to relieve abdominal pain. We analyzed the data with a univariate analysis and a multivariate stepwise regression analysis to determine independent influences on the rate of opioid prescribing. RESULTS: Of 582 children included in the analysis, 53 (9%) received opioid analgesia. Pain in the right lower quadrant on examination, documentation of a pain score in triage, and the level of acuity as determined by the triage nurse were predictors of administration of opioids by the physician. Thirty-four (77%) of the opioids given were below the recommended dose for the child. CONCLUSIONS: Few pediatric patients with abdominal pain are treated with pain medications. The decision to use opioid analgesia for acute abdominal pain in the pediatric ED is influenced by acuity level, pain score documentation in triage, and location of abdominal pain. Efforts should be made to educate physicians on the appropriate administration and dose of opioids in children with abdominal pain in the ED.
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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 | 0.000 | 0.006 |
| 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 | 0.000 | 0.000 |
| 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".