Self-Reported Pain Intensity and Associated Distress in Children Aged 4-18 Years on Admission, Discharge, and One-Week Follow Up to Emergency Department
Bibliographic record
Abstract
OBJECTIVES: Pain is the most common complaint among children presenting to the Emergency Department (ED), yet it is poorly managed. Although the poor management of pain has been documented, no studies have simultaneously determined the distress caused by the presenting pain nor have children been followed after the ED visit to determine whether the pain and distress have resolved. The purpose of this study was to describe pain intensity, distress from pain, and treatment of pain in children presenting to the ED and to follow them 1 week later to describe resolution of their pain. METHODS: A survey design with follow up of patients identified with pain in 2 urban university-affiliated pediatric EDs with children between ages 4 and 18 (N = 533). Measures used included the Coloured Analogue Scale (CAS) for both pain and distress related to pain, mobility problems related to pain, and interference with activities of daily living due to pain. Chart reviews were conducted for documentation of pain assessment and analgesic administration and prescription at discharge. RESULTS: Half of the children presenting were experiencing pain due to musculoskeletal injury and two-thirds of the pain problems had an onset within 48 hours of presentation to the ED. Mean pain intensity on admission was 5.2 (SD 2.3) and at discharge was 4.1 (SD 2.7), however, 22% had worsening of pain and for 26%, the pain remained the same. On admission, 12.8% reported pain intensity 8/10 or more but 23% reported distress levels 8/10 or more. Only 39% received analgesics during the visit and 11% were given a prescription for analgesics at discharge. Children (n = 104) were reached 1 week following discharge from ED and only 5% were reporting pain of 4/10 or more but, of those reporting any pain at all, 34% reported distress from their pain of 4/10 or more. CONCLUSIONS: A greater proportion of children report high intensity of distress from pain than of pain intensity itself when in the ED. Only a small proportion of children received analgesics during the visit to the ED and only slightly more on discharge. Although pain seems to resolve by 1 week, distress is less likely to have resolved. More attention needs to be paid both to pain children are experiencing in the ED and equally to the accompanying distress.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".