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Record W2807290300 · doi:10.1093/cid/ciy480

Using In-depth History Screening as an Additional Method to Help Delabel Inappropriate β-Lactam Allergies

2018· letter· en· W2807290300 on OpenAlexaff
A Vaisman, Janine McCready, Jeff Powis

Bibliographic record

VenueClinical Infectious Diseases · 2018
Typeletter
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsToronto East General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineAllergyIntensive care medicineImmunology

Abstract

fetched live from OpenAlex

To the Editor—We commend Blumenthal et al [1] for their study demonstrating that self-reported β-lactam allergies are associated with poorer outcomes in the perioperative setting. This work adds to the growing literature showing the harms secondary to the use of alternative second-line therapies, which are often broader, costlier, more toxic, and less effective [2, 3]. Blumenthal et al also described several approaches to verifying the unreliable self-reported β-lactam allergies in the perioperative setting, including routine skin testing and specialist consultation and exposure to test doses of cefazolin [4, 5]. Unfortunately, these resources are not available expeditiously in many healthcare centers. We would like to highlight an additional method to help delabel inappropriate β-lactam allergies that is available to all clinicians—that of using in-depth history screening [6]. At our site, each patient presenting to the preoperative clinic with a reported β-lactam allergy underwent a brief assessment by a nurse or pharmacist to clarify the nature, timing, and precise exposure eliciting the reported allergy. Each assessment was reviewed with an infectious diseases physician and patients were deemed safe to proceed with β-lactam prophylaxis if they did not describe a history of type I/immunoglobulin E–mediated reaction or other severe reaction. Antibiotic prophylaxis orders (with approval by the surgical team) were scheduled into the computerized order entry system to be given before the first incision of the upcoming operation. We found, that, among 485 patients with self-reported β-lactam allergy, only 117 (24%) reported a history consistent with anaphylaxis, a figure smaller than that determined by Blumenthal et al [1] (approximately 40%). Using our assessment, 277 patients (57%) ended up receiving β-lactam prophylaxis, with none subsequently experiencing adverse reactions. After implementation of this process at our institution, the overall use of alternative antibiotic prophylaxis at our institution among those reporting a β-lactam allergy decreased from 82% to 56%, and this decrease was directly associated with the number of monthly assessments. Because access to skin testing and allergist consultation is not readily available for the large volumes of elective surgeries performed yearly in most centers, this interdisciplinary approach can provide an efficient solution to the problem well demonstrated by Blumenthal et al A simple screening tool using the principles of prospective audit and feedback can increase the use of β-lactam perioperative prophylaxis without any adverse events and without the use of skin testing. Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0050.003

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.

Opus teacher head0.164
GPT teacher head0.438
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractno

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