A Novel Method of Estimating Small Airway Disease Using Inspiratory-to-Expiratory Computed Tomography
Bibliographic record
Abstract
BACKGROUND: Disease accumulates in the small airways without being detected by conventional measurements. OBJECTIVES: To quantify small airway disease using a novel computed tomography (CT) inspiratory-to-expiratory approach called the disease probability measure (DPM) and to investigate the association with pulmonary function measurements. METHODS: Participants from the population-based CanCOLD study were evaluated using full-inspiration/full-expiration CT and pulmonary function measurements. Full-inspiration and full-expiration CT images were registered, and each voxel was classified as emphysema, gas trapping (GasTrap) related to functional small airway disease, or normal using two classification approaches: parametric response map (PRM) and DPM (VIDA Diagnostics, Inc., Coralville, IA, USA). RESULTS: The participants included never-smokers (n = 135), at risk (n = 97), Global Initiative for Chronic Obstructive Lung Disease I (GOLD I) (n = 140), and GOLD II chronic obstructive pulmonary disease (n = 96). PRMGasTrap and DPMGasTrap measurements were significantly elevated in GOLD II compared to never-smokers (p < 0.01) and at risk (p < 0.01), and for GOLD I compared to at risk (p < 0.05). Gas trapping measurements were significantly elevated in GOLD II compared to GOLD I (p < 0.0001) using the DPM classification only. Overall, DPM classified significantly more voxels as gas trapping than PRM (p < 0.0001); a spatial comparison revealed that the expiratory CT Hounsfield units (HU) for voxels classified as DPMGasTrap but PRMNormal (PRMNormal- DPMGasTrap = -785 ± 72 HU) were significantly reduced compared to voxels classified normal by both approaches (PRMNormal-DPMNormal = -722 ± 89 HU; p < 0.0001). DPM and PRMGasTrap measurements showed similar, significantly associations with forced expiratory volume in 1 s (FEV1) (p < 0.01), FEV1/forced vital capacity (p < 0.0001), residual volume/total lung capacity (p < 0.0001), bronchodilator response (p < 0.0001), and dyspnea (p < 0.05). CONCLUSION: CT inspiratory-to-expiratory gas trapping measurements are significantly associated with pulmonary function and symptoms. There are quantitative and spatial differences between PRM and DPM classification that need pathological investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".