Types and frequency of triggers experienced by asthma patients: A quantitative study
Bibliographic record
Abstract
Background: Detailed information about the frequency and type of asthma trigger exposures recognised by asthma patients is not readily available. Methods: We used patient diaries and an online study to quantify triggers identified by patients with asthma in France, Germany, Italy, Spain and the UK. The study was completed by 1202 adults with asthma on maintenance therapy; 177 also completed an online diary every other day for 3 weeks. Results: The majority of patients in the study were uncontrolled (76% had an Asthma Control Test score of ≤19). A wide variation in the number of triggers ever encountered by asthma patients was identified (1–36, mean 13); over one-third described having experiencing at least 16 triggers. Key triggers and the proportion of sufferers reporting ever experiencing them were: dust/dusting (72%), colds/influenza/sinusitis (69%), smoking (60%), smoke (59%), air pollution (58%), exercise (54%), strong odours (54%), weather changes (51%), mould (51%) and animals (50%). Dust/dusting was the most common and frequent trigger, whereas colds/influenza/sinusitis although common was experienced less frequently. Whilst many patients experienced a range of triggers, the combination of only 3 (dust/dusting, smoking and exercise) accounted for 79% of triggers experienced in the last 2–3 months. Diary entries showed 67% of asthma patients experienced at least one trigger on every day of diary completion. Only (7%) claimed to experience triggers infrequently (every 4–6 months or less often). Conclusion: A wide range and high frequency of asthma triggers amongst asthma patients was identified. The most common were dust/dusting, colds/influenza/sinusitis, smoking and smoke. Funded by GSK
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".