An investigation into biomarkers for the diagnosis of ABPA and aspergillus disease in cystic fibrosis
Bibliographic record
Abstract
Abstract Background Allergic bronchopulmonary aspergillosis (ABPA) is a hypersensitivity lung disease due to Aspergillus fumigatus (Af) which occurs in 10% of patients with cystic fibrosis (CF). ABPA is associated with increased morbidity and accelerated lung function decline; however, existing diagnostic criteria are nonspecific and diagnosis remains challenging. As ABPA is driven by Th2 inflammation, the aim of this study was to evaluate exhaled nitric oxide (FE NO), eosinophilic cationic protein (ECP), peripheral eosinophil count, and bronchodilator response (BDR) in patients with CF. Methods A prospective observational cohort study of pediatric CF patients in a tertiary center. Patients had a clinical and serologic ABPA assessment, FENO, serum ECP, peripheral eosinophil count, and assessment of BDR. Patients were stratified into three groups; ABPA, Af sensitized (AFS), and non‐ABPA non‐Af‐sensitized (non‐AFS). Results A total of 62 patients were included in the study: 13% ABPA, 19% AFS, and 68% non‐AFS. Mean FENO was higher in the ABPA group at 37.8 ppb compared to AFS 15.1 ppb (P = .05) and non‐AFS 13.7 ppb (P = .04). Mean peripheral eosinophil count in ABPA group was also higher at 1000 cells/uL, compared to AFS 221 cells/uL (P = .03) and non‐AFS 220 cells/uL (P = .03). Mean BDR in ABPA group was 13% compared to 5.5% in non‐AFS (P = .01). Serum ECP was higher in patients with ABPA positive compared to the other groups, although this was not statistically significant. Conclusion In children with cystic fibrosis, FENO and peripheral eosinophil counts are elevated in ABPA compared to those that are just sensitized to Aspergillus and may serve as useful diagnostic tests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".