Changes in Airway Dimensions on Computed Tomography Scans of Children with Cystic Fibrosis
Bibliographic record
Abstract
RATIONALE: In cystic fibrosis (CF), chronic bacterial infection and inflammation lead to progressive airway wall thickening and lumen dilatation. OBJECTIVES: To quantify airway wall thickening and lumen dilatation in children with CF over a 2-year interval. METHODS: Children with CF (n = 23) who had two computed tomography (CT) scans (CT(cf1) and CT(cf2)) combined with pulmonary function tests (PFTs), with a 2-year interval between measurements, were compared with control subjects (n = 21) who had one CT (CT(controls)). On cross-sectional cut airway-artery pairs, airway wall area (WA), airway lumen area (LA) and perimeter, and arterial area (AA) were quantified. LA/AA (= marker of bronchiectasis), airway wall thickness (AWT), and WA/AA (= markers of wall thickness) were calculated. CT scans were scored using four different scoring systems. PFTs were expressed as percent predicted. RESULTS: Airway WA-to-AA ratio was 1.45 (p < 0.001) and airway LA-to-AA ratio was 1.92 times higher (p < 0.001) in children with CF compared with age-matched control subjects. LA/AA and WA/AA remained unchanged from CT(cf1) to CT(cf2) and did not increase with age. AWT as a function of airway size increased from CT(cf1) to CT(cf2) by 2% (0.03 mm; p = 0.02). The change in AWT was inversely related to the change in forced expiratory flow between 25 and 75% of expiratory VC (p = 0.002). CONCLUSIONS: In CF, quantitative measurements of airways on CT scans show an increased ratio between airway LA and AA and progressive airway wall thickening. Scoring systems show progression of bronchiectasis but unchanged AWT. PFTs remained stable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".