National Patterns of Codeine Prescriptions for Children in the Emergency Department
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: National guidelines have recommended against codeine use in children, but little is known about prescribing patterns in the United States. Our objectives were to assess changes over time in pediatric codeine prescription rates in emergency departments nationally and to determine factors associated with codeine prescription. METHODS: We performed a serial cross-sectional analysis (2001-2010) of emergency department visits for patients ages 3 to 17 years in the nationally representative National Hospital Ambulatory Medical Care Survey. We determined survey-weighted annual rates of codeine prescriptions and tested for linear trends over time. We used multivariate logistic regression to identify characteristics associated with codeine prescription and interrupted time-series analysis to assess changes in prescriptions for upper respiratory infection (URI) or cough associated with two 2006 national guidelines recommending against its use for these indications. RESULTS: The proportion of visits (N = 189 million) with codeine prescription decreased from 3.7% to 2.9% during the study period (P = .008). Odds of codeine prescription were higher for children ages 8 to 12 years (odds ratio [OR], 1.42; 95% confidence interval [1.21-1.67]) and among providers outside the northeast. Odds were lower for children who were non-Hispanic black (OR, 0.67 [0.56-0.8]) or with Medicaid (OR, 0.84 [0.71-0.98]). The 2006 guidelines were not associated with a decline in codeine prescriptions for cough or URI visits. CONCLUSIONS: Although there was a small decline in codeine prescription over 10 years, use for cough or URI did not decline after national guidelines recommending against its use. More effective interventions are needed to prevent codeine prescription to children.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".