Changes in airway inflammation during pulmonary exacerbations in patients with cystic fibrosis and primary ciliary dyskinesia
Bibliographic record
Abstract
Lung disease in patients with both primary ciliary dyskinesia (PCD) or cystic fibrosis (CF) is associated with impaired mucociliary clearance; however, clinical outcomes are typically worse in CF patients. We assessed whether CF and PCD patients differ in inflammatory response in the airways during pulmonary exacerbation.We first studied clinically stable PCD patients with a spectrum of bacterial pathogens to assess inflammatory response to different pathogens. Subsequently, PCD and CF patients with similar bacterial pathogens were studied at the time of a pulmonary exacerbation and after 21 days of antibiotics treatment. Qualitative and quantitative microbiology, cell counts, interleukin-8 concentrations, and neutrophil elastase activity were assessed in sputum samples obtained before and after treatment.In stable PCD patients, no significant differences were found in sputum inflammatory markers between individuals colonised with different bacterial pathogens. Pulmonary exacerbation severity assessed by a pulmonary exacerbation score and lung function decline from their previous baseline did not differ between CF and PCD patients. Bacterial density for Staphylococcus aureus and Haemophilus influenzae was higher in CF versus PCD (p<0.05), but absolute neutrophil counts were higher in PCD patients (p=0.02). While sputum elastase activity was similar in PCD and CF at the time of exacerbation, it decreased with antibiotic therapy in PCD (p<0.05) but not CF patients.PCD patients differ from those with CF in their responses to treatment of pulmonary exacerbations, with higher neutrophil elastase activity persisting in the CF airways at the end of treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".