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Record W4320716996 · doi:10.1016/j.jaip.2023.02.002

Who Needs Epinephrine? Anaphylaxis, Autoinjectors, and Parachutes

2023· review· en· W4320716996 on OpenAlexafffund
Timothy E. Dribin, Susan Waserman, Paul Turner

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2023
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMcMaster University
FundersMedical Research CouncilNIHR Imperial Biomedical Research CentreNational Institutes of HealthNational Institute for Health and Care ResearchPfizerFood Allergy CanadaMylanNational Center for Advancing Translational SciencesAimmune Therapeutics
KeywordsEpinephrineAnaphylaxisMedicineIntensive care medicineAnesthesiaAllergyMedical emergencyImmunology

Abstract

fetched live from OpenAlex

International guidelines stipulate that intramuscular (IM) epinephrine (adrenaline) is the first-line treatment for anaphylaxis, with an established good safety profile. The availability of epinephrine autoinjectors (EAI) has greatly facilitated the lay administration of IM epinephrine in community settings. However, key areas of uncertainty remain around epinephrine usage. These include variations in prescribing EAI, what symptoms should prompt epinephrine administration, whether emergency medical services (EMS) need to be contacted after administration, and whether epinephrine administered via EAI reduces mortality from anaphylaxis or improves quality of life measures. We provide a balanced commentary on these issues. There is increasing recognition that a poor response to epinephrine, particularly after 2 doses, is a useful marker of severity and the need for urgent escalation. It is likely that patients who respond to a single epinephrine dose do not require EMS activation or emergency department transfer, but data are needed to demonstrate the safety of this approach. Lastly, patients at risk of anaphylaxis must be counseled against over-reliance on EAI alone.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch 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 score0.979
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.005
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 head0.087
GPT teacher head0.440
Teacher spread0.353 · 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

Citations42
Published2023
Admission routes2
Has abstractyes

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