Cystic fibrosis lung disease starts in the small airways: Can we treat it more effectively?
Bibliographic record
Abstract
The aims of this article are to summarize existing knowledge regarding the pathophysiology of small airways disease in cystic fibrosis (CF), to speculate about additional mechanisms that might play a role, and to consider the available or potential options to treat it. In the first section, we review the evidence provided by pathologic, physiologic, and imaging studies suggesting that obstruction of small airways begins early in life and is progressive. In the second section we discuss how the relationships between CF transmembrane conductance regulator (CFTR), ion transport, the volume of the periciliary liquid layer and airway mucus might lead to defective mucociliary clearance in small airways. In addition, we discuss how chronic endobronchial bacterial infection and a chronic neutrophilic inflammatory response increase the viscosity of CF secretions and exacerbate the clearance problem. Next, we discuss how the mechanical properties of small airways could be altered early in the disease process and how remodeling can contribute to small airways disease. In the final section, we discuss how established therapies impact small airways disease and new directions that may lead to improvement in the treatment of small airways disease. We conclude that there are many reasons to believe that small airways play an important role in the pathophysiology of (early) CF lung disease. Therapy should be aimed to target the small airways more efficiently, especially with drugs that can correct the basic defect at an early stage of disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".