Antibiotic Prescribing for Viral Respiratory Infections in the Pediatric Emergency Department and Urgent Care
Bibliographic record
Abstract
BACKGROUND: Viral acute respiratory tract infections (vARTI) are a frequent source of inappropriate antibiotic prescribing. We describe the prevalence of antibiotic prescribing for vARTI in the pediatric emergency department (ED) and urgent care (UC) within a health system, and identify factors associated with overall and broad-spectrum antibiotic prescribing. METHODS: Retrospective chart review within a single pediatric referral health system. Visits of patients, 3 months- 17 years old, with a discharge diagnosis of a vARTI from 2010 to 2015. Data collected included specific vARTI diagnosis, site type (ED or UC), provider type [pediatric emergency medicine subspecialist or physicians, nurse practitioners, physician assistants (non-PEM)] and discharge antibiotics. Odds ratios and 95% confidence intervals (CI) were calculated where appropriate. RESULTS: There were 132,458 eligible visits, mean age 4.1 ± 4.3 years. Fifty-three percent were treated in an ED. Advanced practice providers, a term encompassing nurse practitioners and physician assistants, were the most common provider type (47.7%); 16.5% of patients were treated by a pediatric emergency medicine subspecialist. Antibiotics were prescribed for 3.8% (95% CI: 3.72-3.92) of children with vARTI; 25.4% (95% CI: 24.2-26.6) of these were broad-spectrum, most commonly first-generation cephalosporins (11%; 95% CI 10.2-11.9). Patients treated in an ED or by a non-PEM and those receiving chest radiograph (CXR) received antibiotics most frequently. Prescribing rates varied by specific vARTI diagnosis. CONCLUSIONS: Patients discharged from the pediatric ED or UC with vARTI receive inappropriate antibiotics at a lower rate than reported in other community settings; however, they frequently receive broad-spectrum agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".