The impact of antibiotic allergy labels on antibiotic exposure, clinical outcomes, and healthcare costs: A systematic review
Bibliographic record
Abstract
OBJECTIVE: A growing body of evidence suggests that antibiotic allergy labels as documented in medical records are a risk factor for poor clinical outcomes. In this systematic review, we aimed to determine how antibiotic allergy labels influence 3 domains: antibiotic use and exposure, clinical outcomes, and healthcare-related costs. DESIGN: We performed a systematic review to identify studies reporting outcomes in patients with antibiotic allergy labels compared to nonallergic counterparts. The search included PubMed, EMBASE, Cochrane CENTRAL, EBSCO, Cochrane Database of Abstracts of Reviews of Effects and Web of Science. Two reviewers independently screened studies for inclusion and abstracted data. Studies were graded using the Newcastle-Ottawa quality assessment scale. Study outcomes included antibiotic use, clinical outcomes, and economic outcomes. RESULTS: In total, 41 studies met our criteria for inclusion. These studies varied in medical specialty, patient population, healthcare delivery system, and design, but most were conducted among adults age >18 years (85%) in the inpatient setting (82.5%). Among 34 studies examining antibiotic exposure, 32 (94%) found that patients with antibiotic allergy labels received more broad-spectrum antibiotics. Moreover, 31 studies examined clinical outcomes such as length of hospitalization, ICU admission, hospital readmission, multidrug-resistant or opportunistic infection, or mortality, and 27 (87%) found that allergy-labeled patients had at least 1 negative outcome. Of 9 studies examining healthcare costs, 7 (78%) found that allergy-labeled patients incurred significantly higher drug or hospital-related costs. CONCLUSIONS: Antibiotic allergy labels have negative effects on antibiotic use, clinical outcomes, and economic outcomes in a variety of clinical settings and populations.
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".