Profil leicht verdampfender organischer Substanzen in der Ausatemluft von Kindern mit Mukoviszidose
Bibliographic record
Abstract
Background Breath analysis by electronic nose technology has shown differences between healthy individuals and those with cystic fibrosis (CF), but whether these differences can be explained by airway infection alone is currently unclear. Methods In this cross-sectional observational study, a cloud-connected eNose, the SpiroNose, was used for breath profile analysis of clinically stable paediatric CF patients with airway microbiology cultures positive or negative for CF pathogens. Data-analysis involved advanced signal processing, ambient correction and statistics based on linear discriminant analysis and receiver operating characteristics (ROC) analysis. Results Breath from 100 children with CF (median ppFEV 1 91%) and 25 age matched healthy controls (HC) were analysed. The eNose distinguished between CF patients and HC with high accuracy (96.0%, AUC-ROC 0.985, 95% CI 0.966-1); similar differences from HC were also seen for CF with no CF pathogens (91.1%, AUC-ROC 0.994, CI 0.979-1) and for CFTR modulator treated patients (94.5%, AUC-ROC 0.999, CI 0.994-1). Within the CF group, eNose distinguished between those with airway cultures positive for any CF pathogen and no CF pathogens (79.0%, AUC-ROC 0.791, CI 0.669-0.913), as well as Staphylococcus aureus (SA) only vs. no CF pathogens on culture (78.1%, AUC-ROC 0.788, CI 0.665-0.91). Conclusions Differences in breath profiles between children with CF and controls cannot be explained by presence of CF pathogens alone. Breath profiles of CF patients with SA in airway cultures are distinct from those with no infection, suggesting the utility of eNose technology in the detection of this early CF pathogen in children with CF. Publication History Article published online: 21 September 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".