Distribution of airway smooth muscle remodelling in asthma: Relation to airway inflammation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Pathological phenotypes of asthma have been based predominantly on inflammation, rather than airway wall remodelling. Differences in the distribution of airway smooth muscle (ASM) remodelling between large and small airways may affect clinical outcomes in asthma. The aim of this study was to examine the distribution of ASM remodelling and its relation to airway inflammation. METHODS: Post-mortem cases of asthma (n = 68) were categorized by the distribution of increased thickness of the ASM layer (relative to nonasthmatic controls, n = 37), into 'large only' (LO, n = 15), 'small only' (SO, n = 4) 'large/small' (LS, n = 24) or no increase (NI, n = 25). Subject characteristics, ASM and airway wall dimensions and inflammatory cell numbers were compared between groups. RESULTS: Apart from reduced clinical severity of asthma in NI cases (P = 0.002), subject characteristics did not distinguish asthma groups. Compared with control subjects, ASM cell number, reticular basement membrane thickness, airway wall thickness, percent muscle shortening and eosinophil number were increased (P < 0.05) in both large and small airways in LS cases and only the large airways in LO cases. Increased numbers of neutrophils were observed only in the small airways of LO cases. CONCLUSIONS: Distinct distributions of ASM remodelling are seen in asthma. Pathology limited to the small airways was uncommon. Increased thickness of the ASM layer was associated with airway remodelling and eosinophilia, but not neutrophilia. These data support the presence of distinct pathological phenotypes based on the site of increased ASM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".