What Do We Know about Asthma Triggers? A Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: For patients with asthma, exacerbations and poor control can result from exposure to environmental triggers, such as allergens and air particulates. This study reviewed the international literature to determine whether a global checklist of common asthma triggers might be feasible for use as a research or management tool in clinical practice. METHODS: Literature published from 2002 to 2012 was identified through PubMed and EMBASE using the following search terms: asthma, asthma triggers, prevalence, among others. A total of 1046 abstracts were found; 85 articles were reviewed covering six continents (number of articles): Africa (1), Asia (22), Australia (1), Europe (27), North America (22), and South America (4). RESULTS: The literature consistently pointed to asthma triggers as one contributor to poor asthma control. Frequently cited triggers were similar across countries/regions and included allergens (particularly pollens, molds, dust, and pet dander), tobacco smoke, exercise, air pollutants/particulates, weather patterns/changes, and respiratory infections. Definitions of asthma triggers, how triggers are taken into account in definitions of asthma control, and scientific inquiry into optimal management techniques for triggers were inconsistent and sparse. CONCLUSIONS: Given the apparent importance of triggers in attaining and maintaining asthma control, empirical research concerning optimal trigger management is needed. Results demonstrate that asthma triggers are similar across continents, suggesting a global checklist of triggers for use in research and clinical practice would be feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".