Distribution of increased airway smooth muscle thickness and airway inflammation in asthma
Bibliographic record
Abstract
Rationale: A study of 10 cases of fatal asthma (Ebina et al.) identified two types of asthma, based on the site of increased airway smooth muscle (ASM) thickness: large airways only (due to hyperplasia), or both large and small airways (predominantly hypertrophy). Aim: To determine if this site-specific increase in ASM thickness was reproducible in a larger cohort, and was related to remodeling and airway inflammation. Methods: Postmortem cases of asthma (n=43) were categorised as large only (LO, n=15), small only (SO, n=4) and large/small (LS, n=24) if the mean thickness of the ASM layer in their large or small airways was more than one standard deviation above the mean value of controls (n=37). ASM cell size and number, airway dimensions and airway inflammation were compared between LS and LO cases and controls. Results: In LS cases, ASM cell number and volume, thickness of the reticular basement membrane (RBMt), airway wall thickness and eosinophil area density, but not neutrophil area density were increased (p<0.05) in large and small airways compared with controls. In LO cases, ASM cell number, RBMt and eosinophil area density were increased (p<0.05) only in large airways compared with controls. In addition, neutrophil area density was increased (p<0.05) in both large and small airways compared with controls. There were no significant differences in duration, age of onset of asthma or asthma severity between cases. Conclusions: Phenotypes of asthma, based on the distribution of increased thickness of ASM layer are reproducible and associated with ASM hyperplasia and hypertrophy, remodeling and eosinophilia. Neutrophilia was observed only in the LO cases, suggesting a distinct phenotype.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".