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Record W2091009675 · doi:10.1002/ppul.10335

Inhaled corticosteroids in childhood asthma: Income differences in use

2003· article· en· W2091009675 on OpenAlexaffabout
Anita L. Kozyrskyj, Cameron Mustard, F. Estelle R. Simons

Bibliographic record

VenuePediatric Pulmonology · 2003
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of TorontoUniversity of ManitobaManitoba Health
Fundersnot available
KeywordsMedicineAsthmaMedical prescriptionSocioeconomic statusCorticosteroidSpecialtyPediatricsInhaled corticosteroidsPopulationInternal medicineFamily medicineEnvironmental healthPharmacology

Abstract

fetched live from OpenAlex

Asthma hospitalization rates in children increase with decreasing level of household income. This research was undertaken to determine whether use of inhaled corticosteroid drugs, which can prevent asthma hospitalizations, followed a similar socioeconomic gradient in children with asthma. We performed a cross-sectional study of association, using population-based prescription and healthcare data sources. Our subjects were 16,862 Manitoba children, aged 5-15 years, with prescriptions for asthma drugs during January 1995-March 1996. Our measures were adjusted for asthma severity, physician specialty, and proportion of children with an inhaled corticosteroid prescription by neighborhood income. Forty-five percent of children treated for asthma had at least one inhaled corticosteroid prescription during January 1995-March 1996. The proportion of children with inhaled corticosteroid prescriptions decreased with successive decreases in neighborhood income. The socioeconomic gradient in the likelihood of an inhaled corticosteroid prescription was most evident among children with mild-moderate asthma who were not in the care of an asthma specialist. In conclusion, a socioeconomic gradient in the use of inhaled corticosteroids prescriptions can be found among children with universal access to healthcare and drug insurance.

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)0.001
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 score0.002
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.243
Teacher spread0.229 · 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

Citations25
Published2003
Admission routes2
Has abstractyes

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