Severe exacerbations predict excess lung function decline in asthma
Bibliographic record
Abstract
Severe asthma exacerbations are periods of intense airway inflammation that have been hypothesised to contribute to structural changes in the airways. If so, accelerated lung function decline over time should be more prevalent in adult patients with asthma who have frequent exacerbations than those without, but to date this has not been demonstrated. A cohort study was performed in order to investigate the effect of severe exacerbations on the progression of airway obstruction in 93 nonsmoking asthmatics with moderate-to-severe disease prior to treatment with inhaled corticosteroids. Subjects were followed for > or =5 yrs (median follow-up 11 yrs). In total, 56 (60.2%) subjects experienced at least one severe exacerbation (median rate 0.10.yr(-1)). Oral corticosteroid use and more severe airway obstruction at baseline were associated with a higher exacerbation rate. Independent of these variables, asthma patients with frequent exacerbations had a significantly larger annual decline in forced expiratory volume in one second (FEV(1); median difference (95% confidence interval) 16.9 (1.5-32.2) mL.yr(-1)). Exacerbation rate significantly predicted an excess decline in FEV(1), such that one severe exacerbation per year was associated with a 30.2 mL greater annual decline in FEV(1). These data support the hypothesis that exacerbations, indicating intermittent periods of worsening airway inflammation, are associated with excess lung function decline in asthma.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".