Mucus plugging in severe asthma
Bibliographic record
Abstract
Mucus plugging (MP) plays a crucial role in asthma as it is involved in airflow obstruction, inflammation, and airway remodelling mechanisms. Advances in imaging techniques, such as high-resolution chest CT and hyperpolarised gas MRI, have improved the assessment of MP, linking its burden to disease severity and treatment response. While these radiological modalities provide valuable insight into mucus obstruction, their routine application in clinical practice remains limited. Similarly, novel inflammatory biomarkers have emerged as promising tools for evaluating airway mucus, though their clinical use requires further validation. Mucus-targeting treatments, including biologics that target inflammatory pathways, mucolytics and airway clearance techniques, have shown promising effects. A personalised approach, integrating diagnostic tools, functional evaluation and targeted therapies, is key to mitigating the impact of MP. Clinical case studies demonstrate the complexity of MP pathophysiology and the need for tailored interventions to optimise outcome. Further understanding of polysaccharide biology, the biophysical properties of hydrogels, mucus hydration, epithelial ion transport and mucociliary clearance mechanisms is necessary to improve therapies for MP. Cite as: Venegas Garrido C, Svenningsen S, Nair P. Mucus plugging in severe asthma. In: Jackson DJ, McDonald VM, Pavord ID, eds. Asthma (ERS Monograph). Sheffield, European Respiratory Society, 2025; pp. 48–66 [ https://doi.org/10.1183/2312508X.10013624 ].
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".