Hot Off the Press: Please Stop, Prescribing – Antibiotics for Viral Acute Respiratory Infections
Bibliographic record
Abstract
I nappropriate antibiotic use exposes patients to opportunistic infections, accelerates the development of antibiotic resistant bacteria, and leads to adverse drug events.1 Acute respiratory infections (ARIs) are a major cause of unnecessary antibiotic use.Emergency departments (EDs) in the United States write 10 million antibiotic prescriptions each year, approximately half of which are inappropriate.2-4 Given these risks, strategies to reduce inappropriate antibiotic use in the ED and urgent care center (UCC) are needed.Despite recognizing the need for antibiotic stewardship by EDs and emergency providers, this has not led to practice change.5,6 Providers in the ED and UCC setting are faced with numerous challenges that may limit change, including frequent interruptions, boarding and overcrowding, frequent patient handoffs, and the need to see high volumes of patients.7-9 There is evidence both in the medical literature and in economic theory to support using a package of feedback, nudges, and peer comparisons to improve prescribing outcomes.This has been shown to reduce unnecessary antibiotic prescribing in primary care, and in one study of peer comparisons in outpatient clinics and doctor's offices, these improvements were sustained for at least 12 months after the interventions were completed.10-12
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.225 | 0.078 |
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".