Quantitative evaluation of CF airways using HRCT, µCT and histology
Bibliographic record
Abstract
Aim: To study large and small airway abnormalities in end-stage cystic fibrosis (CF). Methods: 10 CF explant lungs and 6 control (C) lungs were inflated to TLC, frozen at -80°C and scanned using high resolution computed tomography (HRCT) (1 mm slices, max resolution 0.6 mm). Lung tissue cores (1.4 cm x 2 cm) were randomly excised, processed and scanned at a resolution of 8.4 µm using a MicroComputerized Tomography (µCT) device (Skyscan 1172). Using Osirix 4.1 and as previously described (Verleden et al, AJRCCM 2014), we manually assessed the number and size of (visible) open and obstructed airways per generation on HRCT, and the number and size of terminal bronchioles (TB) on µCT. From selected regions in the cores histological sections were sliced. Results: Compared to C, CF had more visible airways on HRCT (567 vs 310; p 0.007). Airway dilatation started from generation 6 (total cumulative airway diameter 203 vs 78cm, p 0.0005). From generation 6 on, around 40% of airways per generation were obstructed. The number of visible airways correlated with the Brody bronchiectasis subscore (R 0.64; p 0.045). In CF versus C, open TB were reduced in number (2.9 vs 5.7/ml; p 0.002) and size (212 vs 363 µm, p 0.004). Closed TB (46%) were fibrotic in 34 or collapsed in 12 % resp. Histology showed narrowed small airways distal to bronchiectasis with disappearance of the lumen and replacement of the airway wall by scar tissue. In 1/5 of studied airways, more distal reorganization into airways with open lumen occurred. Conclusion: End-stage CF causes airway obstruction and dilatation from airway generation 6 on. Distally there is decrease in number and narrowing of TB. Remodeling of small airways was seen on µCT and histology.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".