On the disposition of airway mucus-plugs in asthma before and after treatment
Bibliographic record
Abstract
Background: Asthma airway mucus-occlusions are associated with airflow obstruction, eosinophilic inflammation, ventilation abnormalities, exacerbations and poor asthma control (Dunican, JClinInvest, 2018; Svenningsen, CHEST, 2019). Understanding the distribution of mucus-plugs over time and in response to therapy may provide insight on their potential role as a treatable trait. We quantified mucus-plugs in asthma patients, pre- and post-treatment and compared these findings with mucus burden in healthy volunteers. Methods & results: 100 participants were evaluated including healthy volunteers (n=42), and patients with moderate-severe asthma (n=31; treated with FF/UMEC/VI (200/62.5/25µg)) and severe asthma (n=27; treated with anti-IL-5Rα). All participants provided written informed consent to spirometry, oscillometry, 129Xe MRI and chest CT. Airway mucus was quantified and classified as either stubby or stringy. Prior to treatment, mucus burden was significantly greater in the asthma subgroups (p<.03) compared to healthy volunteers. Asthma therapy reduced mucus-score in both subgroups (anti-IL-5Rα: Δ3±4; FF/UMEC/VI: Δ2±3). Post-treatment mucus-score/-count was 1/1 in moderate-severe and 1/2 in severe asthma patients, similar to healthy volunteers (1/1). In asthma, stringy plugs were more prevalent in asthma patients (70/294;23%) as compared to healthy volunteers (2/49;4%, p<.01). Post-treatment, the burden of stringy plugs in asthma patients was significantly reduced (4/25;10%). Conclusions: Treatment of asthma with either anti-IL-5Rα and FF/UMEC/VI significantly reduced mucus burden similar to healthy volunteers. These results support the notion that airway mucus may serve as a treatable trait in asthma.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".