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Record W2292007809 · doi:10.1155/2010/679716

Role of Age at Asthma Diagnosis in the Asthma-Obesity Relationship

2010· article· en· W2292007809 on OpenAlexaffabout
Shilpa Dogra, Joseph Baker, Chris I. Ardern

Bibliographic record

VenueCanadian Respiratory Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineAsthmaObesityOverweightBody mass indexOdds ratioLogistic regressionPediatricsNational Health and Nutrition Examination SurveyOddsInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether age at asthma diagnosis has an impact on the previously described relationship between asthma and obesity. METHODS: Data were provided from Cycle 1.1 (2000⁄2001) of the Canadian Community Health Survey, a nationally representative health survey that included 6871 participants (2464 males and 4407 females) with asthma. Body mass index was used to categorize participants as normal weight (18.5 kg/m2 to 24.9 kg/m2), overweight (25 kg/m2 to 29.9 kg/m2) or obese (30 kg/m2 or greater). Multivariate logistic regression analyses were used to estimate the odds of overweight and obesity by self-reported age at asthma diagnosis, after accounting for current age and other covariables. RESULTS: In fully adjusted models, males diagnosed with asthma during adolescence (12 to 20 years of age) were at elevated odds of obesity (OR 1.58; 95% CI 1.03 to 2.43) compared with asthmatic patients diagnosed during childhood (0 to 11 years of age). Women diagnosed with asthma in mid life (21 to 44 years of age) and later life (45 to 64 years of age) were 43% (OR 1.43; 95% CI 1.08 to 1.90) and 56% (OR 1.56; 95% CI 1.00 to 2.44) more likely to be obese than those diagnosed in childhood, respectively. CONCLUSIONS: The impact of age at asthma diagnosis on the asthma-obesity relationship differed between males and females. However, the identification of high-risk groups of asthmatic patients may strengthen primary prevention strategies for obesity and related comorbidities at multiple levels of influence.

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)0.001
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 score0.273
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.018
GPT teacher head0.259
Teacher spread0.240 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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