Modeling severe uncontrolled asthma: Transitioning away from health states
Bibliographic record
Abstract
Background: Models developed to date to simulate long-term outcomes of asthma have been criticized for lacking granularity and ignoring disease heterogeneity. Objective: To propose an alternative approach to modeling asthma and apply it to model long-term outcomes in a population with moderate-to-severe type 2 asthma (patients with raised fractional exhaled nitric oxide or eosinophils) and treated with conventional therapy. Methods: A discretely integrated condition event (DICE) approach was adopted, simulating individual profiles with asthma over patients' lifetime in terms of exacerbations, asthma-related death, and death unrelated to asthma. The timing of these events is dependent on profile characteristics including lung function, asthma control, exacerbation history, and other baseline characteristics or contextual factors. Predictive equations were derived from a clinical trial to model time to exacerbation, change in asthma control, lung function, and utility. Real-world studies were used to supplement data gaps. Outcomes evaluated included life expectancy, quality-adjusted life-years (QALY), number of exacerbations, and lung function over time. Results: Average annual rates of severe and moderate exacerbations were 1.82 and 3.08 respectively, with rates increasing over time. Lung function declined at a higher rate compared with the general population. Average life expectancy was 75.2 years, compared with 82.4 years in a matched general population. The majority of life-years were spent with uncontrolled asthma and impaired lung function. Conclusion: Patients with moderate-to-severe type 2 asthma and a history of exacerbations suffer from frequent exacerbations and reduced lung function and life expectancy. Capturing multiple conditions to simulate long-term outcomes in patients with asthma may provide more realistic projections of exacerbation rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".