A Novel Air Trapping Segment Score Identifies Opposing Effects of Obesity and Eosinophilia on Air Trapping in Asthma
Bibliographic record
Abstract
Abstract Rationale Density thresholds in computed tomography (CT) lung scans quantify air trapping (AT) at the whole-lung level but are not informative for AT in specific bronchopulmonary segments. Objectives To apply a segment-based measure of AT in asthma to investigate the clinical determinants of AT in asthma. Methods In each of 19 bronchopulmonary segments in CT lung scans from 199 patients with asthma, AT was categorized as present if lung attenuation was less than −856 Hounsfield units at expiration in ≥15% of the lung area. The resulting AT segment score (0–19) was related to patient outcomes. Measurements and Main Results AT varied at the lung segment level and tended to persist at the patient and lung segment levels over 3 years. Patients with widespread AT (≥10 segments) had more severe asthma (P < 0.05). The mean (±SD) AT segment score in patients with a body mass index ≥30 kg/m2 was lower than in patients with a body mass index <30 kg/m2 (3.5 ± 4.6 vs. 5.5 ± 6.3; P = 0.008), and the frequency of AT in lower lobe segments in obese patients was less than in upper and middle lobe segments (35% vs. 46%; P = 0.001). The AT segment score in patients with sputum eosinophils ≥2% was higher than in patients without sputum eosinophilia (7.0 ± 6.1 vs. 3.3 ± 4.9; P < 0.0001). Lung segments with AT more frequently had airway mucus plugging than lung segments without AT (48% vs. 18%; P ≤ 0.0001). Conclusions In patients with asthma, air trapping is more severe in those with airway eosinophilia and mucus plugging, whereas those who are obese have less severe trapping because their lower lobe segments are spared.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".