Airway wall thinness and COPD: Analysis of spatially comparable airways. The MESA COPD study
Bibliographic record
Abstract
The relationship of airway remodelling to COPD is poorly understood. COPD is associated with loss of peripheral airways and reduced airway lumen dimensions, which may introduce bias when sampling airways to evaluate wall thickness. We assessed the relationship between airway wall thickness and COPD using spatially comparable bronchi. The MESA COPD Study recruited smokers aged 50–79 yrs. Spatially comparable airways were defined by 3 approaches:anatomic name, generation number and distance from trachea. Airway dimensions were quantified by CT using APOLLO software (Vida). Analyses were adjusted for age, sex, body size, race and lung volume. Among 314 participants, 47% had COPD. Comparing anatomically matched airways, there were greater odds of COPD with decreasing wall thickness(Table). Similar associations were obtained matching on generation or distance from the trachea. In contrast, airways sampled by lumen diameter were more proximal in COPD compared to controls(p<0.001), and the association with wall thickness was reversed(Table). Table: Odds of COPD per standard deviation DECREMENT in airway wall thickness (95%CI) Anatomically matched bronchi Trachea: 1.1 (0.7to1.6) Mainstems: 1.3 (1.0to1.5) Lobar: 1.2 (1.0to1.4) Segmental: 1.3 (1.2to1.5) Subsegmental: 1.4 (1.2to1.6) Lumen diameter matched bronchi >11mm: 1.0 (0.9to1.2) 11to9mm: 0.9 (0.8to1.2) 9to7mm: 0.8 (0.7to0.9) 7to5mm: 0.7 (0.6to0.8) 5to3mm: 0.8 (0.8to0.9) Analysis of spatially comparable airways demonstrated increased odds of COPD with decreasing wall thickness. In contrast, sampling airways by lumen diameter resulted in selection of more proximal airways in COPD compared to controls and introduced bias in the assessment of airway wall dimensions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".