Modeling stochastic and spatial heterogeneity in a human airway tree to determine variation in respiratory system resistance
Bibliographic record
Abstract
Asthma is a variable disease with changes in symptoms and airway function over many time scales. Airway resistance (Raw) is variable and thought to reflect changes in airway smooth muscle activity, but just how variation throughout the airway tree and the influence of gas distribution abnormalities affect Raw is unclear. We used a multibranch airway lung model to evaluate variation in airway diameter size, the role of coherent regional variation, and the role of gas distribution abnormalities on mean Raw (Raw) and variation in Raw as described by the SD (SDRaw). We modified an anatomically correct airway tree, provided by Merryn Tawhai (The University of Auckland, New Zealand), consisting of nearly 4,000 airways, to produce temporal and spatial heterogeneity. As expected, we found that increasing the diameter variation by twofold, with no change in the mean diameter, increased SDRaw more than fourfold. Perhaps surprisingly, Raw was proportional to SDRaw under several conditions-when either mean diameter was fixed, and its SD varied or when mean diameter varied, and SD was fixed. Increasing the size of a regional absence in gas distribution (ventilation defect) also led to a proportionate increase in both Raw and SDRaw. However, introducing regional dependence of connected airways strongly increased SDRaw by as much as sixfold, with little change in Raw. The model was able to predict previously reported Raw distributions and correlation of SDRaw on Raw in healthy and asthmatic subjects. The ratio of SDRaw to Raw depended most strongly on interairway coherent variation and only had a slight dependence on ventilation defect size. These findings may explain the linear correlation between variation and mean values of Raw but also suggest that regional alterations in gas distribution and local coordination in ventilation amplify any underlying variation in airway diameters throughout the airway tree.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".