Pulmonary CT and MRI markers of asthma remission
Bibliographic record
Abstract
On-treatment asthma remission has emerged as a treatment goal in the therapeutic management of severe asthma. Clinical remission of asthma—characterized by sustained symptom control, stable lung function, no oral steroid use or exacerbations—is well-described, but remains a contentious goal of therapy. For example, it is not known if remission is accompanied by the reversal of airway remodeling, nor is it known how long therapy is required for lung structural and functional normalization to occur. In this regard, complete asthma remission considers patients in whom clinical remission and improved airway inflammation (blood and sputum eosinophil counts, fraction of exhaled nitric oxide) and histopathological airway wall and lumen markers (ie, epithelium, basement membrane and smooth muscle) occur. Hence, the Global Initiative for Asthma (GINA) and other frameworks highlight the need to integrate cellular and imaging markers as well as pathology within remission criteria and definitions. Here, we present an overview of current remission definitions and their post-hoc analyses in asthma clinical trials as well as describe retrospective pulmonary imaging data and an image-based index of the reversal of airway and pulmonary vascular remodeling, observed in asthma. The Western-Imaging-Index (WIIN) harnesses the sensitivity and specificity of 129Xe magnetic resonance imaging (MR)I and computed tomography (CT) airway (ventilation-defect-percent, mucus-occlusions, wall thickness, lumen area, total airway count) and pulmonary vascular (small vessel blood volume) measurements. By integrating a novel imaging-index of lung normalization within the definition of asthma remission, we provide a way to untangle the relationship between clinical and complete remission in asthma patients on biologic therapy.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".