Pathological Comparisons of Paraseptal and Centrilobular Emphysema in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Abstract Rationale Although centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are commonly identified on multidetector computed tomography (MDCT), little is known about the pathology associated with PSE compared with that of CLE. Objectives To assess the pathological differences between PSE and CLE in chronic obstructive pulmonary disease (COPD). Methods Air-inflated frozen lung specimens (n = 6) obtained from patients with severe COPD treated by lung transplantation were scanned with MDCT. Frozen tissue cores were taken from central (n = 8) and peripheral (n = 8) regions of each lung, scanned with micro–computed tomography (microCT), and processed for histology. The core locations were registered to the MDCT, and a percentage of PSE or CLE was assigned by radiologists to each of the regions. MicroCT scans were used to measure number and structural change of terminal bronchioles. Furthermore, microCT-based volume fractions of CLE and PSE allowed classifying cores into mild emphysema, CLE-dominant, and PSE-dominant. Measurements and Main Results The percentages of PSE measured on MDCT and microCT were positively associated (P = 0.015). The number of terminal bronchioles per milliliter of lung and cross-sectional lumen area were significantly lower and wall area percentage was significantly higher in CLE-dominant regions compared with mild emphysema and PSE-dominant regions (all P < 0.05), whereas no difference was found between PSE-dominant and mild emphysema samples (all P > 0.5). Immunohistochemistry showed significantly higher infiltration of neutrophils (P = 0.002), but not of macrophages, CD4, CD8, or B cells, in PSE compared with CLE regions. Conclusions The terminal bronchioles are relatively preserved, whereas neutrophilic inflammation is increased in PSE-dominant regions compared with CLE-dominant regions in patients with COPD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".