Epidemiology and natural history of<i>Pseudomonas aeruginosa</i>airway infections in non-cystic fibrosis bronchiectasis
Bibliographic record
Abstract
The natural history and epidemiology ofPseudomonas aeruginosainfections in non-cystic fibrosis (non-CF) bronchiectasis is not well understood. As such it was our intention to determine the evolution of airway infection and the transmission potential ofP. aeruginosain patients with non-CF bronchiectasis. A longitudinal cohort study was conducted from 1986–2011 using a biobank of prospectively collected isolates from patients with non-CF bronchiectasis. Patients included were ≥18 years old and had ≥2 positiveP. aeruginosacultures over a minimum 6-month period. All isolates obtained at first and most recent clinical encounters, as well as during exacerbations, that were morphologically distinct on MacConkey agar were genotyped by pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST). A total of 203 isolates from 39 patients were analysed. These were compared to a large collection of globally epidemic and local CF strains, as well as non-CF isolates. We identified four patterns of infection in non-CF bronchiectasis including: 1) persistence of a single strain (n=26; 67%); 2) strain displacement (n=8; 20%); 3) temporary disruption (n=3; 8%); and 4) chaotic airway infection (n=2; 5%). Patterns of infection were not significant predictors of rates of lung function decline or progression to end-stage disease and acquisition of new strains did not associate with the occurrence of exacerbations. Rarely, non-CF bronchiectasis strains with similar pulsotypes were observed in CF and non-CF controls, but no CF epidemic strains were observed. While rare shared strains were observed in non-CF bronchiectasis, whole-genome sequencing refuted patient–patient transmission. We observed a higher incidence of strain-displacement in our patient cohort compared to those observed in CF studies, although this did not impact on outcomes.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".