Oral Antibiotic Use for Otitis Media with Effusion
Bibliographic record
Abstract
OBJECTIVES: (1) To evaluate the probability of antibiotic administration associated with ICD-9 diagnosis of otitis media with effusion (OME) in the absence of acute otitis media, (2) to determine whether usage varies according to visit setting, and (3) to ascertain if practice gaps are such that future practice changes might be measured. STUDY DESIGN: Cross-sectional analysis of an administrative database. SETTING: Ambulatory visits in the United States. SUBJECTS AND METHODS: National Ambulatory and Hospital Ambulatory Medical Care Surveys, 2005-2010; univariate, multivariate, and stratified analyses of antibiotic usage were performed. The study population was restricted to children without acute or unspecified otitis media. The primary outcome was the probability of oral antibiotic administration when OME was diagnosed. The impact of the location of service and subspecialty care was also analyzed. RESULTS: Data from 1,390,404,196 pediatric visits demonstrated that oral antibiotics were administered for 32% of visits with an OME diagnosis, even in the absence of acute otitis media (odds ratio, 4.31; 95% confidence interval: 2.88-6.44; P < .001). The highest antibiotic administration was seen in the emergency department (risk difference, 37.1%; number needed to harm, 3). No significant increased risk of antibiotic usage was seen during otolaryngology visits. Diagnoses of infections at nonotologic sites were associated with a 1.98 to 26.60 increase in odds of oral antibiotic administration. CONCLUSION: Oral antibiotics continue to be administered in children with OME in the absence of acute infection, with risk varying by location of service. There is a potential opportunity for quality improvement through reducing antibiotic administration for pediatric OME.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".