Ascertainment of Small Airways Dysfunction Using Oscillometry to Better Define Asthma Control and Future Risk
Bibliographic record
Abstract
The small airways comprise generations 8 to 23 of the bronchial tree, consist of airways with an internal diameter < 2 mm, and are classically difficult to assess and treat in persistent asthma. Small airways dysfunction (SAD) is integral to the asthma management paradigm because it is associated with poorer symptom control and greater levels of type 2 inflammation, and it has been proposed as a potentially treatable asthma trait. Although identification of SAD based on oscillometry has been found to be clinically useful in managing asthma, very few physicians, including specialists, use this technique as part of standard or adjunct evaluation of lung function to diagnose asthma, grade severity of airway obstruction, ascertain disease control or the risk for future exacerbations, or to make management decisions. To rectify the unrecognized value of oscillometry in the asthma community, a consortium of authors who are investigators with knowledge and experience of oscillometry wished to address the most important clinical questions raised by our colleagues who are considering using this technique, including its clinical utility. In this article, we discuss integral concepts, including applicability of oscillometry as a predictive tool for asthma exacerbations and disease control, adequacy of spirometry and oscillometry in assessing SAD, potential limitations of oscillometry, and treatment options for SAD.
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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) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".