Differences in airway inflammation between children with primary ciliary dyskinesia (PCD) and cystic fibrosis (CF)
Bibliographic record
Abstract
PCD and CF lung disease are similar in many aspects, but may differ in inflammatory responses. We compared markers of airway inflammation in patients with mild PCD or CF pulmonary exacerbation. Methods: Patients with an established diagnosis of CF or PCD were recruited from clinic if able to do pulmonary function testing and produce sputum. P. aeruginosa or B. cepacia complex infections were among the exclusion criteria. Sputum was obtained before (V1) and after (V2) 21 days of oral antibiotic treatment and analyzed for qualitative and quantitative micobiology, cell counts, interleukin-8 (IL-8) concentrations and neutrophil elastase activity. Results: Eleven PCD and 16 CF patients were included. Mean age was 10 (range 7-16) yrs in both groups. Mean (±SD) FEV1 at V1 was 63 (±13.8) % in PCD and 74 (±18.3) % predicted in CF (p=0.1). FEV1 improved to 71 (±21.2) % in PCD (p<0.05, paired t-test) and 86 (±12.3) % predicted in CF (p<0.01) on V2. Combined S. aureus and H. influenza density in sputum at V1 was significantly lower in PCD than CF. Sputum cell counts were not different. Mean (±SEM) IL-8 levels in sputum were similar in PCD and CF on V1 (237.3±77.8 vs 118.4 ±27.7 ng/ml, p=0.09) and V2 (211.5±105.2 vs 138.4±35.0 ng/ml, p=0.9). In contrast, while sputum elastase activity was similar in PCD and CF at V1 (83.0 ±15.0 vs 61.5±14.8 nanomol/min, p=0.16) it decreased at V2 in PCD (20.9±8.0, p<0.05) but not CF (88.1±37.0 nanomol/min). Conclusions: Children with PCD differ from CF patients in their responses to treatment of a mild pulmonary exacerbation. While lung function improved in both groups, neutrophil elastase persisted at higher levels in CF 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".