Optimizing preoperative prophylaxis in patients with reported β-lactam allergy: a novel extension of antimicrobial stewardship
Bibliographic record
Abstract
Background: Use of alternative second-line antibiotics is associated with adverse events in patients reporting β-lactam allergy. In the perioperative setting, we hypothesized that structured allergy histories, without the use of skin testing, can reduce alternative prophylactic antibiotic use. Objectives: Assess the impact of structured allergy histories on patients with self-reported β-lactam allergy (SRBA) undergoing elective surgical procedures. Methods: Structured allergy histories were performed by a pharmacist and reviewed with an infectious diseases physician. Patients were deemed safe to proceed with cefazolin prophylaxis if they did not describe a history of type I-mediated or severe reaction. Antibiotic prophylaxis orders (with approval by the surgical team) were scheduled into the computerized order entry system to be given prior to first incision of the operation. Results: Of the 485 patients with SRBA that underwent structured allergy histories, 117 (24.1%) reported a type I-mediated allergy history; 267 (55.1%) patients received cefazolin prophylaxis and none subsequently experienced an adverse reaction. After intervention implementation, the overall use of alternative antibiotic prophylaxis at Michael Garron Hospital (Toronto, Canada) among those with SRBA decreased from 81.9% to 55.9%. This drop was associated with the number of monthly assessments (P < 0.001) in a regression analysis. Conclusions: Using a simple structured history and the principles of prospective audit and feedback, we were able to increase the use of cefazolin perioperative prophylaxis without any serious adverse events and in the absence of skin testing or diagnostic challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".