Computational fluid dynamics of small airway disease in chronic obstructive pulmonary disease
Bibliographic record
Abstract
BACKGROUND: Small airways (<2 mm diameter) are major sites of airflow obstruction in chronic obstructive pulmonary disease (COPD). This study aimed to quantify the impact of small airway disease, characterized by narrowing, occlusion, and obliteration, on airflow parameters in smokers and end-stage patients with COPDs. METHODS: We performed computational fluid dynamics (CFD) simulations of inspiratory airflow in three lung groups: control non-used donor lungs (no smoking/emphysema history), non-used donor lungs with a smoking history and emphysema, and explanted end-stage COPD lungs. Each group included four lungs, with two tissue cylinders. Micro-CT-scanned small airways were segmented into 3D models for CFD simulations to quantify pressure, resistance, and shear stress. CFD results were benchmarked against simplified linear and Weibel models. FINDINGS: CFD simulations showed higher pressures in COPD vs. controls (p = 0.0091) and smokers (p = 0.015), along with increased resistance (p = 0.0057 vs. controls; p = 0.0083 vs. smokers) and up to a tenfold rise in shear stress (p = 0.010 vs. controls). Narrowing and occlusion were shown to independently increase pressure, resistance, and shear stress, which were validated through segmentation corrections. Pressures and resistance assessed with simplified models were up to seven-fold higher for smokers and even 72 higher for COPD compared with CFD values. INTERPRETATION: These findings show that increased airflow parameters can explain the association between small airway disease and airflow limitation in COPD, underscoring small airway vulnerability. Additionally, they highlight the limitations of theoretical models in accurately capturing small airway disease. FUNDING: Supported by the KU Leuven (C16/19/005).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".