Resources Assessment for Penicillin Allergy Testing Performed by Pharmacists at the Patient’s Bedside
Bibliographic record
Abstract
BACKGROUND: False penicillin allergies lead to increased antimicrobial resistance, adverse effects, and health care costs by promoting the use of broad-spectrum antibiotics. The Infectious Diseases Society of America recommends the implementation of allergy testing. OBJECTIVES: The primary objective of this research was to estimate the number of pharmacist full-time equivalents (FTEs) required for an intervention aimed at determining penicillin allergy in hospitalized patients. Acceptance of pharmacists' suggestions on antibiotic therapy are described. METHODS: A quasi-experimental study was conducted in a 712-bed university hospital involving hospitalized patients with a suspected penicillin allergy and an infection treatable with penicillin. The time required for the intervention, which included a questionnaire, penicillin allergy testing (skin-prick test, intradermal injection, and oral provocation test), and recommendations on antibiotic therapy were measured to calculate the number of pharmacist FTEs. RESULTS: A total of 55 patients were included. Scarification allergy testing was performed on 37, intradermal allergy test on 33, and oral provocation test on 26 patients. The intervention ruled out penicillin allergy in 26 patients, with no serious adverse effects. The intervention was associated with a median weekly pharmacist FTE of 0.15 (interquartile range = 0.12-0.25). The acceptance of pharmacists' suggestions was high and led to 9 patients being switched to an antibiotic with a narrower spectrum of activity. CONCLUSIONS AND RELEVANCE: This study describes penicillin allergy testing and the number of median weekly hospital pharmacist FTEs required, which was approximately 0.15. These data may aid in the implementation of this safe intervention that promotes narrower-spectrum antibiotherapy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".