Breath profiles in cystic fibrosis children treated with CFTR modulators
Bibliographic record
Abstract
Background: Exhaled breath profiles (BPs) differ between patients with cystic fibrosis (CF) and healthy controls (HC). It is not known whether these differences are caused by airway colonization with CF pathogens or other factors. Aims and objectives: To investigate whether electronic nose (eNose) BPs of HC were different from a) CF patients with negative airway microbiology or b) CF patients treated with CFTR modifier therapy. Methods: In this cross-sectional observational study, BPs were collected from clinically stable paediatric CF patients attending routine CF clinic for follow-up and compared to age-matched HC. A cloud-connected eNose, SpiroNose (de Vries et al. 2018 ERJ) was used for BP analysis. Data-analysis involved advanced signal processing, ambient correction and statistics based on linear discriminant analysis and ROC analysis. Results: 100 clinically stable children with CF were included (median ppFEV1 91%, age 12.0 years). The eNose was able to distinguish between HC (n=25) and all CF (accuracy 96.0%, AUC 0.985, 95%CI 0.966-1) as well as HC and CF patients with usual airway flora (n=20) (91.1%, AUC 0.994, 95%CI 0.979-1). BPs of 30 patients on CFTR modulator therapies (7 ivacaftor, 9 ivacaftor/lumacaftor, 5 ivacaftor/tezacaftor, 9 ivacaftor/tezacaftor/elexacaftor) were also different from HC (94.5%, AUC 0.999, 95%CI 0.994-1). There was no difference in BPs between CF patients treated or not treated with CFTR modulators. Conclusions: Differences in BPs between CF and HC cannot be explained by airway colonization with CF pathogens and CFTR modulator therapy does not seem to normalize BPs. Further studies are needed to identify other factors contributing to the unique BPs of people with CF.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".