Airway Wall Thickness Assessed Using Computed Tomography and Optical Coherence Tomography
Bibliographic record
Abstract
RATIONALE: Computed tomography (CT) has been shown to reliably measure the airway wall dimensions of medium to large airways. Optical coherence tomography (OCT) is a promising new micron-scale resolution imaging technique that can image small airways 2 mm in diameter or less. OBJECTIVES: To correlate OCT measurements of airway dimensions with measurements assessed using CT scans and lung function. METHODS: Forty-four current and former smokers received spirometry, CT scans, and OCT imaging at the time of bronchoscopy. Specific bronchial segments were identified and measured using the OCT images and three-dimensional reconstructions of the bronchial tree using CT. MEASUREMENTS AND MAIN RESULTS: There was a strong correlation between CT and OCT measurements of lumen and wall area (r = 0.84, P < 0.001, and r = 0.89, P < 0.001, respectively). Compared with CT, OCT measurements were lower for both lumen and wall area by 31 and 66%, respectively. The correlation between FEV(1)% predicted and CT and OCT measured wall area (as percentage of the total area) of fifth-generation airways was very strong (r = -0.79, r = -0.75), but the slope of the relationship was much steeper using OCT than using CT (y = -0.33x + 82, y = -0.1x + 78), indicating greater sensitivity of OCT in detecting changes in wall measurements that relate to FEV(1). CONCLUSIONS: OCT can be used to measure airway wall dimensions. OCT may be more sensitive at detecting small airway wall changes that lead to FEV(1) changes in individuals with obstructive airway disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".