Quantitative Computed Tomography Measures of Emphysema and Airway Wall Thickness Are Related to Respiratory Symptoms
Bibliographic record
Abstract
RATIONALE: There is limited knowledge about the relationship between respiratory symptoms and quantitative high-resolution computed tomography measures of emphysema and airway wall thickness. OBJECTIVES: To describe the ability of these measures of emphysema and airway wall thickness to predict respiratory symptoms in subjects with and without chronic obstructive pulmonary disease (COPD). METHODS: We included 463 subjects with chronic obstructive pulmonary disease (COPD) (65% men) and 488 subjects without COPD (53% men). All subjects were current or ex-smokers older than 40 years. They underwent spirometry and high-resolution computed tomography examination, and completed an American Thoracic Society questionnaire on respiratory symptoms. MEASUREMENTS AND MAIN RESULTS: Median (25th percentile, 75th percentile) percent low-attenuation areas less than -950 Hounsfield units (%LAA) was 7.0 (2.2, 17.8) in subjects with COPD and 0.5 (0.2, 1.3) in subjects without COPD. Mean (SD) standardized airway wall thickness (AWT) at an internal perimeter of 10 mm (AWT-Pi10) was 4.94 (0.33) mm in subjects with COPD and 4.77 (0.29) in subjects without COPD. Both %LAA and AWT-Pi10 were independently and significantly related to the level of dyspnea among subjects with COPD, even after adjustments for percent predicted FEV(1). AWT-Pi10 was significantly related to cough and wheezing in subjects with COPD, and to wheezing in subjects without COPD. Odds ratios (95% confidence intervals) for increased dyspnea in subjects with COPD and in subjects without COPD were 1.9 (1.5-2.3) and 1.9 (0.6-6.6) per 10% increase in %LAA, and 1.07 (1.01-1.14) and 1.11 (0.99-1.24) per 0.1-mm increase in AWT-Pi10, respectively. CONCLUSIONS: Quantitative computed tomography assessment of the lung parenchyma and airways may be used to explain the presence of respiratory symptoms beyond the information offered by spirometry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".