Microbial Epidemiology of the Cystic Fibrosis Airways: Past, Present, and Future
Bibliographic record
Abstract
Abstract Progressive obstructive lung disease secondary to chronic airway infection, coupled with impaired host immunity, is the leading cause of morbidity and mortality in cystic fibrosis (CF). Classical pathogens found in the airways of persons with CF (pwCF) include Pseudomonas aeruginosa, Staphylococcus aureus, the Burkholderia cepacia complex, Achromobacter species, and Haemophilus influenzae. While traditional respiratory-tract surveillance culturing has focused on this limited range of pathogens, the use of both comprehensive culture and culture-independent molecular approaches have demonstrated complex highly personalized microbial communities. Loss of bacterial community diversity and richness, counteracted with relative increases in dominant taxa by traditional CF pathogens such as Burkholderia or Pseudomonas, have long been considered the hallmark of disease progression. Acquisition of these classic pathogens is viewed as a harbinger of advanced disease and postulated to be driven in part by recurrent and frequent antibiotic exposure driven by frequent acute pulmonary exacerbations. Recently, CF transmembrane conductance regulator (CFTR) modulators, small molecules designed to potentiate or restore diminished protein levels/function, have been successfully developed and have profoundly influenced disease course. Despite the multitude of clinical benefits, structural lung damage and consequent chronic airway infection persist in pwCF. In this article, we review the microbial epidemiology of pwCF, focus on our evolving understanding of these infections in the era of modulators, and identify future challenges in infection surveillance and clinical management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".