Types, frequency and impact of asthma triggers on patients’ lives: a quantitative study in five European countries
Bibliographic record
Abstract
OBJECTIVE: To identify the types, frequency and impact of asthma triggers and the relationship to asthma control among adults with asthma in Europe. METHODS: Adults with self-reported physician-diagnosed asthma receiving maintenance asthma treatment and self-reported exposure to known asthma triggers completed an online questionnaire; a subset completed a diary over 3-4 weeks. Information on asthma control (Asthma Control Test™ [ACT]), asthma triggers, frequency of exposure and behaviours in response or to avoid asthma triggers and the perceived impact on daily life was captured. A post-hoc analysis evaluated the impact of high trigger burden on the frequency of severe asthma exacerbations, hospitalisations and days lost at work/study. RESULTS: A total of 1202 adults participated and 177 completed the diary. Asthma was uncontrolled for the majority (76%) of participants and most (52%) reported exposure to 6-15 asthma triggers. As trigger burden increased, behavioural changes to manage trigger exposure had a significantly increased impact on daily life (p < 0.0001) and job choice (p = 0.002). Participants reporting a high trigger burden (>16) were more likely to report uncontrolled asthma than those with a low trigger burden (1-5). Participants with a high trigger burden had previously experienced on average two more severe asthma attacks during a lifetime (p < 0.001), two more hospitalisations (p < 0.001) and 3.5 more missed days at work or study in the last year due to their asthma (p < 0.001) than those with a low trigger burden. CONCLUSIONS: Adults with asthma reporting a high trigger burden (>16 different triggers) experience more severe asthma attacks than those reporting lower trigger burdens.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".