Variables Associated With Administration of Nurse-initiated Analgesia in Pediatric Triage
Bibliographic record
Abstract
OBJECTIVES: Triage nurse-initiated analgesia (TNIA) has been shown to be associated with decreased time to the provision of analgesia and improved patient satisfaction. We examined variables that influence the provision of analgesia in a pediatric emergency department that uses TNIA. METHODS: A 4-year retrospective cohort study of all children with triage pain scores ≥1 was conducted. Data on demographics and patients' and nurses' characteristics were collected. Logistic regression analyses were used to examine the effect of multiple variables on the provision of any analgesia and opioid analgesia. RESULTS: Overall, 28,746 children had triage pain scores ≥1; 14,443 (50.2%) patients received analgesia of any type and 1888 (6.6%) received opioid analgesia. Mean time to any analgesia was 8.0±3.7 minutes. Of the 9415 patients with severe pain, 1857 (19.7%) received opioid analgesia. Age, sex, hourly number of patients waiting to be triaged, and nurse experience were not associated with the provision of any analgesia or opioid analgesia. Severe pain had the highest odds ratios (ORs) for the provision of any analgesia and opioid analgesia (7.7; 95% confidence interval [CI]: 7.1-8.2 and 22.8; 95% CI: 18.1-28.8, respectively). Traumatic injury and time-to-triage <8 minutes were associated with the provision of opioid analgesia (OR: 4.7; 95% CI: 4.2-5.2 and OR: 1.6; 95% CI: 1.5-1.8, respectively). DISCUSSION: TNIA yielded a short time to analgesia, but rates of any analgesia and opioid analgesia were low. Several variables associated with the provision of any analgesia and opioid analgesia were identified. Our findings provide evidence to guide future educational programs in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".