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Record W2131201362 · doi:10.2174/157488409788184981

Management of Food-Induced Anaphylaxis: Unsolved Challenges

2009· review· en· W2131201362 on OpenAlexaff
Katherine Arias, Susan Waserman, Manel Jordana

Bibliographic record

VenueCurrent Clinical Pharmacology · 2009
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsMedicineAnaphylaxisFood allergyIntensive care medicineAnaphylactic reactionsAllergic reactionEmergency departmentMedical emergencyAllergyImmunologyNursing

Abstract

fetched live from OpenAlex

Anaphylaxis is an acute and often severe systemic allergic reaction. The prevalence of food allergy has been increasing and is currently estimated at approximately 3.5%. Food allergic reactions account for one-third to one-half of anaphylaxis cases worldwide. It is estimated that approximately 30,000 food-related anaphylactic reactions are treated in United States emergency departments (ED) every year resulting in approximately 2000 hospitalizations and 150 deaths. The increasing rate of food-induced anaphylactic episodes in the last few decades underlines the existence of major challenges. This review will critically appraise current guidelines for the diagnosis as well as the acute and long-term management of food-induced anaphylaxis (FIA). Importantly, it will outline existing challenges and suggest measures to improve outcomes in patients with FIA. We propose that the discovery of novel diagnostic (i.e. biomarkers and predictors) and therapeutic approaches is a major challenge that may be overcome as the mechanisms underlying FIA are better delineated. We further propose that better dissemination, implementation and compliance with the consensus management guidelines are urgently needed. This will require education of ED personnel, patient empowerment as well as effective multilateral communication among patients, emergency and family physicians, allergists and specialized volunteer organizations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.379
GPT teacher head0.546
Teacher spread0.167 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations16
Published2009
Admission routes1
Has abstractyes

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