The natural history of severe asthma and influences of early risk factors: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Severe asthma is associated with disproportionately high morbidity, but little is known about its natural history and how risk factors at first year of diagnosis modify its subsequent development. METHODS: Using administrative health data, we retrospectively followed patients 14-55 years of age with newly diagnosed severe asthma in British Columbia, Canada. Based on intensity of resource use (drug therapy) and occurrence of exacerbations, each patient-year was classified into mild, moderate, or severe asthma. We estimated the probability of transition between severity levels or to death over the study period using a four-state Markov model, and used this to assess the 10-year trajectory of severe asthma and the influence of baseline risk factors. RESULTS: We followed 13,467 patients. Ten years after incident severe asthma, 83% had transitioned to a less severe level (mild: 43%, moderate: 40%). Low socioeconomic status, high comorbidity burden, and high adherence (proportion of days covered (PDC) by asthma controller therapy) in the first year were independently associated with, respectively, 10%, 24% and 35% more time in severe asthma over the next 10 years. Sex was not associated with the clinical course. CONCLUSIONS: Most patients with incident severe asthma used fewer resources over time, indicating a long-term transition to milder asthma. Potentially modifiable risk factors for poor prognosis of severe asthma include low socioeconomic status and high comorbidity burden. The association between PDC and future asthma severity is likely due to residual confounding by disease severity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".