Patterns of airway disease and the clinical diagnosis of asthma in the Busselton population
Bibliographic record
Abstract
The aim of this study was to examine how objective measures related to lung function cluster in the general population and how the patterns relate to asthma and bronchitis as diagnosed by a doctor (DDA and DDB, respectively). A cross-sectional survey of an age-stratified random general population sample of 1,969 adults from the electoral register of Busselton (Australia) was performed in 2005-2007. Respiratory symptoms, DDA ever, DDB ever, recent wheezing and smoking history, together with anthropometric measurements, forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC), methacholine challenge or bronchodilator response, exhaled nitric oxide (eNO), skin-prick tests to common allergens, and blood eosinophil and neutrophil counts were studied. Cluster analysis (variables sex, age, atopy, FEV₁ % predicted, FEV₁/FVC, airway hyperresponsiveness, eNO, log eosinphil count, log neutrophil count and body mass index) was used to identify phenotypic patterns. Seven clusters (subjects with DDA and DDB, respectively) were identified: normal males (n=467; 7 and 13%), normal females (n=477; 12 and 18%), obese females (n=250; 16 and 28%), atopic younger adults (n=330; 21 and 17%), atopic adults with high eNO (n=130; 30 and 25%), atopic males with reduced FEV₁ (n=103; 33 and 32%) and atopic adults with bronchial hyperreactivity (n=212; 40 and 26%). The clinical diagnosis of asthma (ever) and bronchitis (ever) is not specific for any of the clustering patterns of airway abnormality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".