<i>Pseudomonas aeruginosa</i> Strain-sharing in Early Infection Among Children With Cystic Fibrosis
Bibliographic record
Abstract
BACKGROUND: We previously identified Pseudomonas aeruginosa isolates with characteristics typical of chronic infection in some early infections in children with cystic fibrosis (CF), suggesting that these isolates may have been acquired from other patients. Our objective was to define the extent of P. aeruginosa strain-sharing in early CF infections and its impact on antibiotic eradication treatment failure rates. METHODS: We performed whole genome sequencing on isolates from early pediatric CF pulmonary infections and from the following comparator groups in the same hospital: chronic CF infection, sink drains, sterile site infections, and asymptomatic carriage. Univariate logistic regression was used to assess factors associated with treatment failure. RESULTS: In this retrospective, observational study, 1029 isolates were sequenced. The CF clones strain B and clone C were present. In 70 CF patients with early infections, 14 shared strains infected 29 (41%) patients over 5 years; 16% (n = 14) of infections had mixed strains. In the 70 children, approximately one-third of shared-strain infections were likely due to patient-to-patient transmission. Mixed-strain infections were associated with strain-sharing (odds ratio, 8.50; 95% confidence interval, 2.2-33.4; P = .002). Strain-sharing was not associated with antibiotic eradication treatment failure; however, nosocomial strain transmission was associated with establishment of chronic infection in a CF sibling pair. CONCLUSIONS: Although early P. aeruginosa CF infection is thought to reflect acquisition of diverse strains from community reservoirs, we identified frequent early CF strain-sharing that was associated with the presence of mixed strains and instances of possible patient-to-patient transmission.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".