An observational cohort study comparing ibuprofen and oxycodone in children with fractures
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness and safety of prescribing ibuprofen and oxycodone for at-home management of children's fracture pain. METHODS: A prospective observational cohort was conducted at the Stollery Children's Hospital pediatric emergency department (June 2010-July 2014). Children aged 4-16 years with an isolated fracture discharged home with advice to use either ibuprofen or oxycodone were recruited. RESULTS: A cohort of 329 children (n = 217 ibuprofen, n = 112 oxycodone) were included. Mean age was 11.1 years (SD 3.5); 68% (223/329) were male. Fracture distribution included 80.5% (264/329) upper limb with 34.3% (113/329) requiring fracture reduction. The mean reduction in Faces Pain Score-Revised score (maximum pain-post-treatment pain) for Day 1 was 3.6 (SD 1.9) (ibuprofen) and 3.8 (SD 2.1) (oxycodone) (p = 0.50); Day 2 was 3.6 (SD 1.8) (ibuprofen) and 3.7 (SD 1.6) (oxycodone) (p = 0.56); Day 3 was 3.7 (SD 1.7) (ibuprofen) and 3.3 (SD 1.7) (oxycodone) (p = 0.24). Children prescribed ibuprofen (51.2%, 109/213) experienced less adverse events compared to those prescribed oxycodone (70.5% 79/112) on Day 1 (p = 0.001). Children prescribed ibuprofen (71.8%, 150/209) had their function (eat, play, school, sleep) affected less than those prescribed oxycodone (83.0%, 93/112) (p = 0.03) on Day 1. CONCLUSION: Children prescribed ibuprofen or oxycodone experienced similar analgesic effectiveness for at-home fracture pain. Oxycodone prescribing was associated with more adverse events and negatively impacted function. Oxycodone use does not appear to confer any benefit over ibuprofen for pain relief and has a negative adverse effect profile. Ibuprofen appears to be a safe option for fracture-related pain.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".