The Approach to<i>Pseudomonas aeruginosa</i>in Cystic Fibrosis
Bibliographic record
Abstract
Pseudomonas aeruginosa continues to be the most common pathogen in cystic fibrosis (CF) lung disease, and chronic infection with mucoid strains is associated with an accelerated decline in lung function. Although multiple factors can potentially explain the susceptibility of CF airways to this organism, their individual relevance is still largely unclear. Prevention of infection remains an important task, and hygiene measures have been successful in reducing cross-infection, but the universal presence of the organism creates an ongoing challenge, and vaccination strategies have not been highly successful to date. Over the last decade treatment strategies have shifted from controlling chronic infection to attempting to eradicate P. aeruginosa in the early stages of infection. Multiple strategies have been shown to be efficacious, but the optimal form and duration of therapy have yet to be defined. Inhaled antibiotics are a key component of maintenance therapy for chronic infection, and the spectrum of available compounds is rapidly expanding. Pulmonary exacerbations can be reduced with this strategy but usually require intravenous antibiotic therapy once they occur. Nonantibiotic approaches to address P. aeruginosa infection are currently being developed and may expand the therapeutic repertoire in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".