Sputum Eosinophilia and Magnetic Resonance Imaging Ventilation Heterogeneity in Severe Asthma
Bibliographic record
Abstract
RATIONALE: Inflammation and smooth muscle dysfunction are integral components of severe asthma that contribute to luminal obstruction causing airflow limitation, ventilation heterogeneity, and symptoms. This is important for guiding treatment decisions directed at the inflammatory (e.g., anti-T-helper cell type 2 monoclonal antibodies) and noninflammatory, smooth muscle-mediated (e.g., bronchial thermoplasty) components of severe asthma. OBJECTIVES: To investigate the contribution of eosinophilic bronchitis and smooth muscle dysfunction to magnetic resonance imaging (MRI) ventilation heterogeneity in patients with severe asthma. METHODS: We measured the inhaled hyperpolarized gas MRI response to salbutamol as a marker of smooth muscle dysfunction, and sputum eosinophils as a marker of airway inflammation, and their contributions to ventilation heterogeneity (quantified as the ventilation defect percent [VDP]) in 27 patients with severe asthma. Spirometry and forced oscillation airway resistance measurements were also acquired pre- and postsalbutamol. Patients were dichotomized on the basis of sputum eosinophilia, and pre- and postsalbutamol VDP and physiological measurements were evaluated. MEASUREMENTS AND MAIN RESULTS: , indicating smooth muscle response. In those patients in whom sputum eosinophilia was uncontrolled, greater VDP persisted postsalbutamol (P = 0.004). Postsalbutamol VDP correlated with sputum eosinophils (r = 0.63; P = 0.005). CONCLUSIONS: In patients with severe asthma, MRI regionally identifies the inflammatory and noninflammatory components of airway disease. Ventilation heterogeneity persists postsalbutamol in patients with uncontrolled eosinophilic bronchitis, which may be the functional consequence of airway inflammation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".