The Bronchodilator and Anti‐Inflammatory Effect of Long‐Acting Muscarinic Antagonists in Asthma: An <scp>EAACI</scp> Position Paper
Bibliographic record
Abstract
As cholinergic innervation is a major contributor to increased vagal tone and mucus secretion, inhaled long-acting muscarinic antagonists (LAMA) are a pillar for the treatment of chronic obstructive pulmonary disease and asthma. By blocking the muscarinic receptors expressed in the lung, LAMA improve lung function and reduce exacerbations in asthma patients who remained poorly controlled despite treatment with inhaled corticosteroids and long-acting β2 agonists. Asthma guidelines recommend LAMA as a third controller to be added on before the initiation of biologicals. In addition to bronchodilation, LAMA also exert anti-inflammatory and anti-fibrotic effects by inhibiting muscarinic receptors present in neutrophils, macrophages, fibroblasts and airway smooth muscle cells. Thus, besides bronchodilation, LAMA might provide additional therapeutic effects, thereby supporting an endotype-driven approach to asthma management. The Position Paper, developed by the Asthma Section of the European Academy of Allergy and Clinical Immunology, discusses the main cholinergic pathways in the lung, reviews the findings of significant clinical trials and real-life studies on LAMA use in asthma, examines the placement of these drugs in asthma clinical guidelines, and considers the potential for personalised medicine with LAMA in both adult and paediatric asthma patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".