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Temporal trends in anaphylaxis ED visits over the last decade and the effect of COVID-19 pandemic on these trends

2023· article· en· W6958322815 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsAnaphylaxisEmergency departmentPandemicAllergyRetrospective cohort studyAccidental

Abstract

fetched live from OpenAlex

Anaphylaxis is an acute systemic and potentially fatal allergic reaction. We evaluated trends in yearly rates of anaphylaxis in a pediatric Emergency Department (ED) in Montreal, Canada. A prospective and retrospective recruitment process was used to find families of children who had presented with anaphylaxis at the Montreal Children’s Hospital between April 2011 and April 2021. Using a uniform recruitment form, data were collected. Anaphylaxis patterns were compared to clinical triggers using descriptive analysis. Among 830,382 ED visits during the study period, 2726 (26% recruited prospectively) presented with anaphylaxis. The median age was 6 years (IQR: 0.2, 12.00), and 58.7% were males. The relative frequency of anaphylaxis cases doubled between 2011–2015, from 0.22% (95% CI, 0.19, 0.26) to 0.42 March 2020, the total absolute number of anaphylaxis cases and relative frequency declined by 24 cases per month (p < 0.05) and by 0.5% of ED visits (p < 0.05). The rate of anaphylaxis has changed over the years, representing modifications in food introduction strategies or lifestyle changes. The decrease in the frequency of anaphylaxis presenting to the ED during the COVID pandemic may reflect decreased accidental exposures with reduced social gatherings, closed school, and reluctance to present to ED.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.969

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.075
GPT teacher head0.292
Teacher spread0.217 · 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
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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