Multivariate Asthma Phenotypes in Adults: The Quebec City Case-Control Asthma Cohort
Bibliographic record
Abstract
Background and Objectives: Asthma is a heterogeneous disease where patient severity can be classified according to various models based on numerous variables. Large collections of well-phenotyped subjects are needed to find distinct clusters of patients for personalized medicine and future genetic studies. The objective of this study is to describe the collection of the Quebec City Case-Control Asthma Cohort and to identify homogeneous subgroups of asthma patients based on clinical characteristics. Methods: This cohort is part of an ongoing project initiated in 2007 to elucidate the genetic basis of asthma. All subjects are randomly recruited at the same site following advertisements. Subjects are unrelated French Canadian white adults 18 years of age or older. Each participant underwent a spirometry, methacholine challenge, and allergy skin-prick tests. Blood was collected for DNA, cell counts and total serum IgE measurements. So far, 982 subjects have been recruited and classified as cases (n = 566) or controls (n = 416). We performed factor and cluster analyses on collected phenotypes from this set to identify subgroups of phenotypically similar asthmatic patients. Results: Factor analysis with 13 variables led to the selection of five factors: lung function, numbers of allergens, blood eosinophil percentage, smoking status and age. K-means cluster analysis on the reduced dataset produced four significantly different groups with the following characteristics: smoking history, low atopy and low lung function, high atopy, and young non-smoking with average atopy. Conclusions: The Quebec City Case-Control Asthma Cohort is a new resource for local and collaborative clinical and genetic research on asthma. This new collection reveals distinct multivariate phenotypes of adult asthma that are likely to be important for future genetic studies and targeted therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".