Heterogeneity of Airway Smooth Muscle Remodeling in Asthma
Bibliographic record
Abstract
Abstract Rationale Ventilatory defects in asthma are heterogeneous and may represent the distribution of airway smooth muscle (ASM) remodeling. Objectives To determine the distribution of ASM remodeling in mild–severe asthma. Methods The ASM area was measured in nine airway levels in three bronchial pathways in cases of nonfatal (n = 30) and fatal asthma (n = 20) and compared with control cases without asthma (n = 30). Correlations of ASM area within and between bronchial pathways were calculated. Asthma cases with 12 large and 12 small airways available (n = 42) were classified on the basis of the presence or absence of ASM remodeling (more than two SD of mean ASM area of control cases, n = 86) in the large or small airway or both. Measurements and Main Results ASM remodeling varied widely within and between cases of nonfatal asthma and was more widespread and confluent and more marked in fatal cases. There were weak correlations of ASM between levels within the same or separate bronchial pathways; however, predictable patterns of remodeling were not observed. Using mean data, 44% of all asthma cases were classified as having no ASM remodeling in either the large or small airway despite a three- to 10-fold increase in the number of airways with ASM remodeling and 81% of asthma cases having ASM remodeling in at least one large and small airway. Conclusions ASM remodeling is related to asthma severity but is heterogeneous within and between individuals and may contribute to the heterogeneous functional defects observed in asthma. These findings support the need for patient-specific targeting of ASM remodeling.
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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 | 0.002 |
| 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".