The findings of a clinical surveillance bronchoalveolar lavage programme in pre‐school patients with cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: Evidence suggests infection is present in the lower airways of young children with cystic fibrosis (CF), even when clinically stable. Oropharyngeal samples (OPS) are typically used for airway surveillance in these children but have been shown to have low positive predictive values and low sensitivity in detecting lower airway infection when compared with the reference standard, bronchoalveolar lavage (BAL). METHODS: The aim of this study was to determine the prevalence of pathogens in lower airway samples detected as part of a pilot clinical BAL surveillance programme, in young children aged from one to six years old, and to ascertain if their detection resulted in a change in treatment. RESULTS: During the study 78 bronchoscopies were performed on 38 patients. The average age at the time of bronchoscopy was 2.7 years (range 0.3-7.0 year). A significant organism was detected in 58 (74.5%) BALs. Haemophilus influenzae was detected in 27 (34.6%) samples, 16 (20.5%) samples had Staphylococcus aureus, and nine (11.5%) had Pseudomonas aeruginosa. Change in treatment occurred after 46 (58.9%) BALs. CONCLUSIONS: This study suggests that, in young non-expectorating children with CF, routine surveillance bronchoscopy allows the detection of significant lower airway pathogens and provides the opportunity for targeted treatment of sub-clinical infection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".