Use of analgesia in a paediatric accident and emergency department following limb trauma
Bibliographic record
Abstract
The objective of this study was to assess analgesic use and the use of a pain scoring system on those children presenting to a paediatric accident and emergency (A&E) department with a history of injury due to trauma. A random sample of patients who presented to a paediatric A&E department over a 6-week period with a history of limb trauma were prospectively studied. Pain severity scores were assessed on arrival and at 10, 30 and 60 minutes using the Douhit Faces Scale and any analgesia given or plaster application was noted. One hundred and seventy-two patients were studied. The median age was 10 years (range 3-13 years) and the majority, 56%, were male. The mean initial pain scores were 2.7 (range 1-4) for boys and 3.0 (range 1-4) for girls. The presenting injuries were 103 upper or lower limb fractures and 69 'soft tissue' injuries. Only 84 (49%) patients received analgesic medication in the department (30% morphine; 70% paracetamol); analgesia was not given to the remaining 88 (51%). Of these, 7 declined analgesia, and 5 had already taken analgesia on arrival to A&E. Despite prompt triage (median time 2 minutes, range 0-10 minutes), the median time from arrival to paracetamol administration was 20 minutes (range 4-105 minutes) and for morphine was 14 minutes (range 2-57 minutes). Pain is a common symptom in patients presenting to A&E. Because children's pain can be particularly difficult to assess, a pain scoring system such as the Douhit Faces Scale can be a useful means of pain assessment in the A&E setting. Despite increased awareness, pain is still under treated in the A&E department.
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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 | 0.005 |
| 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 | 0.000 |
| 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".