Quantitative Assessment of the Airway Wall Using Computed Tomography and Optical Coherence Tomography
Bibliographic record
Abstract
Ever since the site and nature of airflow obstruction in chronic obstructive pulmonary disease was described by Hogg, Thurlbeck, and Macklem, investigators have been looking for methods to noninvasively measure the airway wall dimensions. Recent advances in computed tomography technology and new computer algorithms have made it possible to visualize and measure the airway wall and lumen without the need for tissue. However, while there is great hope for computed tomographic assessment of airways, it is well known that the spatial resolution does not allow small airways to be visualized and there are still concerns about the sensitivity of these measurements obtained from these airways. Optical coherence tomography is a new bronchoscopic imaging technique that has generated considerable interest because the spatial resolution is much higher than computed tomography. While relatively more invasive than computed tomography, it has the advantage of not exposing the patient to ionizing radiation. This review discusses some of the data surrounding these two imaging techniques in patients with chronic obstructive pulmonary disease. These imaging techniques are extremely important in the assessment of patients with chronic obstructive pulmonary disease because therapy that is designed to modulate the inflammation in airways may be contraindicated in subjects with the emphysema phenotype and visa versa. Therefore, these new imaging techniques are very likely to play a front-line role in the study of chronic obstructive pulmonary disease and will, hopefully, allow clinicians to phenotype individuals, thereby personalizing their treatment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".