Computed Tomographic Imaging of the Airways in COPD and Asthma
Bibliographic record
Abstract
Computed tomography (CT) is the modality of choice for imaging the airways. Volumetric data sets with isotropic spatial resolution based on multidetector thin-section CT with overlapping reconstruction should be used. Chronic obstructive pulmonary disease and asthma are the 2 most common disease entities that are defined by airflow obstruction. The morphologic correlates of airway changes are dilation of the lumen, thickening of the wall, visibility of small airways due to mucus or edema, air trapping, hypoxic vasoconstriction, and collapsibility. To assess air trapping, additional expiratory low-dose scans are recommended. In clinical routine, these findings are visually assessed and should be routinely reported. However, the interobserver variability is high, and there is a clear need for objective software-based measurements. The development of such tools is challenging, and they are just becoming available on a broader scale. Novel techniques based on dual-energy CT aim to measure iodine distribution maps to assess pulmonary perfusion as well as the distribution of inhaled xenon gas to assess the distribution and time course of pulmonary ventilation. However, these techniques are still being investigated in clinical studies. This review will provide an overview of CT for the diagnosis of chronic obstructive pulmonary disease and asthma, its role in phenotyping these diseases, and the measurement of disease severity and functional compromise.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".