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Record W4404377959 · doi:10.1080/02770903.2024.2429679

Fast-food consumption and asthma-related emergency room visits in California

2024· article· en· W4404377959 on OpenAlexaff
Kimberly Valle, Marcela R. Entwistle, Asa Bradman, Paul Brown, Emanuel Alcala, Ricardo Cisneros

Bibliographic record

VenueJournal of Asthma · 2024
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsInstitute of Health Services and Policy Research
Fundersnot available
KeywordsMedicineAsthmaEmergency departmentConsumption (sociology)Environmental healthMedical emergencyGerontologyEmergency medicineFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Asthma is common, affecting up to 8% of adults in the United States. Several studies have shown an association between poorer diet and asthma. Despite the prevalence of fast-food consumption in the Western diet, research examining fast food consumption and asthma is limited. OBJECTIVE: This study aimed to examine the association between fast food consumption and asthma-related emergency room visits among adults with asthma in California from 2011-2016. METHODS: This cross-sectional study focused on 11,561 adults with asthma in California. Publicly available data from the California Health Interview Survey was used. The independent variable included fast food consumption, and the dependent variable was emergency room visits due to asthma. This study used logistic regression models and controlled for sex, race, self-reported overall health, BMI, and current smoking status. Survey weights were applied to ensure the analysis represented the general population. RESULTS: = 0.03). CONCLUSION: Our findings suggest that high consumption of fast food among adults with asthma may result in higher odds of asthma-related emergency room visits. Thus, decreasing fast food consumption may benefit adults with asthma by reducing emergency room visits.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.439
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.278
Teacher spread0.267 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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